Filtered by themePersonalization & adaptation107 papersclear ✕

A Novel Emotion-Aware Networking Model for Enhanced User Experience in 5G networksViktor Stoynov · 2025The emergence of 5G networks has considerably broadened the capabilities and features of mobile networks. A notable development in this area is the incorporation of emotional intelligence into mobile networks, leading to the proposal of the Emotion-aware Networking Paradigm (ENP) in this paper. ENP strives to equip mobile networks with the ability to identify and understand user emotions and adapt services accordingly. This paper introduces a model for implementing ENP in 5G mobile networks, called the Emotion-aware Networking Model (ENM), detailing its structure, functions, and main characteristics. This paper also explores the challenges and prospects related to ENM and offers examples of potential use cases. Additionally, this paper presents the Emotion-Aware Service and User Profile Management Function (EASUPMF) for ENM, designed to unleash ENM's potential by managing user profiles and delivering personalized emotional services based on emotion recognition. Several new Key Performance Indicators (KPIs) are also established to assess ENM's performance, such as Emotional Diversity, Emotional Alignment, and Emotional Intensity. Our research indicates that ENM could transform the mobile network sector and enhance user experiences by delivering tailored services grounded in user emotions.

UX of AI & PersonalizationResearch Papers

Using user emotions to trigger UI adaptationUniv. Grenoble Alpes · 2025User experience is an important user interface quality factor that needs to be maintained during interaction. With this goal, we propose to adapt user interfaces to users’ emotions. This requires being able to detect at runtime negative emotions to trigger adaptations. However, emotions depend on users’ gender and age. This makes it difficult to identify negative emotions and their cause. This paper studies the categorization of emotions depending on users’ age and gender and two user experience dimensions (aesthetics and usability). Thanks to an experiment, it proposes some emotions thresholds that can be used for detecting usability and aesthetics problems from users’ age and gender. These results are used in a user interface adaptation system to infer negative emotions and user interface problems.

UX of AI & PersonalizationResearch Papers

Advancements in Context Recognition for Edge Devices and Smart Eyewear: Sensors and ApplicationsFrancesca Palermo, Luca Casciano · 2025Edge devices have garnered significant attention for their ability to process data locally, providing low-latency, context-aware services without the need for extensive reliance on cloud computing. This capability is particularly crucial in context recognition, which enables dynamic adaptation to a user’s real-time environment. Applications range from health monitoring and augmented reality to smart assistance and social interaction analysis. Among edge devices, smart eyewear has emerged as a promising platform for context recognition due to its ability to unobtrusively capture rich, multi-modal sensor data. However, the deployment of context-aware systems on such devices presents unique challenges, including real- time processing, energy efficiency, sensor fusion, and noise management. This manuscript provides a comprehensive survey of context recognition in edge devices, with a specific emphasis on smart eyewear. It reviews the state-of-the-art sensors and applications for context inference. Furthermore, the paper discusses key challenges in achieving reliable, low-latency context recognition while addressing energy and computational constraints. By synthesizing advancements and identifying gaps, this work aims to guide the development of more robust and efficient solutions for context recognition in edge computing. INDEX TERMS Context recognition, edge computing, smarteyewear, wearable technology.

UX of AI & PersonalizationResearch Papers

Identifying Hand-based Input Preference Based on Wearable EEGKaining Zhang∗ · 2024Understanding user input preference can improve the user experi- ence, however automatically determining preference can be diffi- cult. In this paper, we designed an EEG-based method for directly evaluating hand-based input preference for touch and mid-air ges- tures on a smartwatch. We conducted a two-phase experiment, recording EEG data from 18 participants as they performed ges- tures and captured their ratings (Phase 1) and preference choices (Phase 2) for each gesture. Our analysis uncovered distinct EEG patterns between preferred and non-preferred gestures, including significant differences in Power Spectral Density (PSD), Coherence (Coh), and Sample Entropy (SE) features. When participants en- gaged with their preferred input gestures, we identified decreased brain activity (PSD) in the central and occipital regions, reduced brain connectivity (Coh) in the delta and alpha bands, and increased brain complexity (SE) in multiple sizes. These insights offer the potential to develop rapid detection of user intent for interactive computing devices by analysing brain signals. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. AHs 2024, April 04–06, 2024, Melbourne, VIC, Australia © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 979-8-4007-0980-7/24/04 https://doi.org/10.1145/3652920.3653028 CCS CONCEPTS • Human-centered computing →User studies; Gestural input; Laboratory experiments.

UX of AI & PersonalizationResearch Papers

Recommendations user interface in web-based e-commerce systemsOleksandr Yeroshkin, Janusz Sobecki · 2024In the world of dynamic e-commerce, competition is fierce, and the ability to attract and retain customers is becoming crucial. In this reality, adaptive user interfaces (UI) play an important role, allowing e-commerce platforms to personalize the shopping experience to better understand and meet customer needs. When combined with advanced artificial intelligence (AI) technologies, adaptive UI can bring revolutionary changes to the way customers interact with e-commerce platforms. In this article, we will discuss how AI can be used to design and improve adaptive user interfaces in e-commerce systems, and what benefits can result from this combination. © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0)) Peer-review under responsibility of the scientific committee of the 28th International Conference on Knowledge Based and Intelligent information and Engineering Systems Oleksandr Yeroshkin et al. / Procedia Computer Science 246 (2024) 2874–2881 2875 tion of an experimental approach in a controlled environment. During the implementation and verification phases, we will be able to generate alternative solutions and evaluate them according to the UX and/or conversion requirements. The paper is structured as follows. In the second section, the state of the art is presented. Then, in the following section, an example of a commercial system whose user interface will be adapted is given. In the fourth section, the proposed solution of adapted UI based on AI is described. In the final section, we summarize the paper and give some information on the proposed future work. 2. State of the art In the beginning, UI recommendation services did not use artificial intelligence methods [12]. According to Baraglia [4] and Kopel [10], mainly traditional approaches were used at that time. However, currently we are dealing with solutions that intensively use various AI algorithms. Automating user interface optimization can be achieved using a variety of techniques based on both traditional ap- proaches and AI and machine learning (ML) algorithms. When it comes to optimizing user experience in e-commerce, there are three approaches: adaptable, semi-adaptive, and fully adaptive [1]. The first involves manual adjustment by a user or expert according to their usage criteria [18]. The second involves manual adjustment supported by system recommendations which is a kind of evol

UX of AI & PersonalizationResearch Papers

Predictive Analytics for Website User BehaviorPawan Vankhede · 2024This study examines the application of predictive analytics to user behavior analysis on websites, with a particular emphasis on a dataset of customer behavior from e-commerce. To forecast behavioral patterns, the study will apply the random forest method. Additionally, exploratory data analysis (EDA) will be carried out to extract insights from the dataset. The dataset employed in this study includes a variety of factors, such as purchase history, browsing habits, and consumer demographics, among other pertinent data. The research attempts to derive useful insights that can improve the comprehension and prediction of user behavior on the website by utilizing predictive analytics approaches. In this study, the random forest algorithm which is well-known for its capacity to manage intricate datasets and generate precise predictions is employed. The method builds several decision trees and combines their predictions to produce more reliable and accurate outcomes by utilizing ensemble learning. Predicting different aspects of user behavior, including browsing patterns, chance of making a purchase, and customer segmentation, is the aim. When paired with the insights from EDA, the outcomes of the behavioral analysis prediction made with the random forest algorithm can provide e-commerce companies with useful data. Gaining insight into user behavior can help with customer engagement, website design optimization, personalization, and overall user experience enhancement.

UX of AI & PersonalizationResearch Papers

Harnessing AI potential in E-Commerce: improving user engagement and sales through deep learning-based product recommendationsZhang · 2024Artificial intelligence (AI) has become a game-changing influence in e-commerce, reshaping the way businesses inter­ act with customers and increasing operational efficiency. In today’s digital era, AI technologies are being progressively embedded into e-commerce platforms to enhance the user experience, improve processes, and drive business growth. One of the primary areas where AI demonstrates its potential is in personalized product recommendations. This study explores the development of AI-driven recommendations regarding product design, sales, and customer experience in e-commerce environments. The research employs a quantitative approach to explore how AI technologies can enhance various facets of online retail. Using purposive sampling, 439 consumers and 356 sellers from diverse regions of China participated in an online survey designed to gather insights into their interactions with AI-driven recommendations. Data analysis was rigorously conducted using Lisrel 10.20 and Smart PLS 3 software, focusing on validating hypotheses related to AI’s influence on consumer behavior and business outcomes. Key findings include AI’s significant role in boosting productiv­ ity, enhancing sales figures, and enriching consumer experiences through personalized recommendations. This research provides a deeper insight into AI’s potential in e-commerce and provides recommendations for businesses focused on using AI technology to increase user engagement and increase sales in digital marketing.

UX of AI & PersonalizationResearch Papers

Computers in Human Behavior Reports2024Computers have gone from being a workplace tool to something we carry in our pockets. This explosion of technological growth has been supported by HCI researchers and product designers as they strive to predict and meet user needs by studying user intentions toward, and experiences with, technology to improve usability. However, psychological research shows that device usage does not come without a psychological cost to the user, an impact apparently under-considered in HCI research. We surveyed users (n = 211) on their feelings about their experiences with different aspects and types of technology when the technology performed contrary to expectations. We found that technology that was frustrating to use or performed below expectations led to users reporting more negative feelings towards their use of the technology. Findings indicate that the user experience could be improved by considering user frustration and feelings towards technology in the design process. Funding sources This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

UX of AI & PersonalizationResearch Papers

Collaborative Filtering and Recommendation Algorithm for Artificial Intelligence Live Streaming E-Commerce Platforms Based on Big DataChen · 2024With the development of the Internet and mobile Internet, live streaming e-commerce has become an emerging e-commerce force. However, traditional recommendation algorithms have shortcomings in terms of accuracy and personalization of recommendation results, and more intelligent and personalized recommendation algorithms need to be applied. This article aimed to achieve personalized product recommendations and enhance the shopping experience of users by analyzing their historical behavioral data, real-time interests and needs, combined with big data and artificial intelligence technology. The collaborative filtering recommendation algorithm based on live streaming had an average recommendation accuracy of over 80% for user groups 1, 2, and 3. The research results of this article had important practical significance for promoting the healthy development of live streaming e-commerce platforms, improving user experience, and enhancing platform competitiveness. © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0)) Peer-review under responsibility of the scientific committee of the 11th International Conference on Applications and Techniques in Cyber Intelligence Yu’e Chen et al. / Procedia Computer Science 247 (2024) 826–833 827 and loyalty to the platform, but also promotes user purchasing behavior, increases purchase conversion rates, and enhances order value. This article analyzes the current application status of traditional recommendation algorithms and big data artificial intelligence in the e-commerce field, points out the shortcomings and areas for improvement of existing methods, and proposes methods such as user collaborative filtering and live streaming collaborative filtering to improve recommendation accuracy. On this basis, an in-depth analysis and exploration of collaborative filtering and recommendation algorithms for intelligent live streaming e-commerce platforms based on big data is conducted. 2. Related Works A large number of customers use traditional e-commerce portal websites, which lack product quality assurance. Rashidin Md Salamun used the status quo bias theory to study customer retention behavior on e-commerce platforms. The research findings can help managers and policy makers develop new policies to better serve customers [1]. Nichifor Eliza aimed to study the impact of using artificial intelligence through chatbots on the con

UX of AI & PersonalizationResearch Papers

Transforming User Experience (UX) through Artificial Intelligence (AI) in Interactive MediaYingchia Liu, Yang Xu, Ranze Song · 2024This paper discusses the application and advantages of artificial intelligence technology in digital media interactive product design. Firstly, the development background of artificial intelligence technology and its promoting effect on design innovation is introduced, and the application of new technologies, such as generative adversarial networks in art creation and design personalization, is definitely analyzed. It then explores in detail the ability of AI to break traditional constraints in interactive design, innovate design, and optimize user experience, especially in digital media. Finally, the case study of Microsoft Cortana shows the method of classifying and processing user queries by machine learning system and its experimental results. The research of this paper provides the theoretical basis and empirical support for the application of artificial intelligence technology in the future interactive design of digital media and has important academic and practical significance.

UX of AI & PersonalizationResearch Papers

Human-Centered AI: Enhancing User Interaction with Intelligent SystemsGalhenage Gayan Sudesh Suranga Perer · 2024This research examines Human- Centered AI (HCAI) and its contribution to user involvement and user experience with intelligent systems in the healthcare, finance, and education industries. HCAI focuses on deploying AI in a fashion that considers human abilities, values, and emotions in a way that enhances convenience, adaptability, and reliability. The study adopts a qualitative and quantitative mixed methodology in conducting surveys and interviews with users and AI developers examining the effects of human- centered design attributes explanation and user control on user satisfaction. The results imply that such systems of AI which follow the principles of human-centered design have more acceptance and endorsement among users. In particular, participants stressed the need for effective articulation of decision-making processes by AI tools and provision for some degree of manual control over the tools. At the same time, the analysis has uncovered the existence of persistent ethical problems, such as bias, privacy, reliability of AI systems, which need to be addressed further. According to the findings, the application of the HCAI approach can greatly boost the experience and confidence of users in using AI devices. Practical implications are related to the go and bias justice, implementing and enhancing information on explainability, feedback, privacy and integrated ethics into the systems. In this paper, we present additional development for the application of ethical artificial intelligence. Inde Term — Adaptability, Explainability, Human-Centered AI, Interaction Design, Personalization, Transparency, Trust, User Experience, Usability

UX of AI & PersonalizationResearch Papers

Article Functional Framework for Multivariant E-Commerce User InterfacesAdam Wasilewski · 2024Modern e-businesses heavily rely on advanced data analytics for product recommendations. However, there are still untapped opportunities to enhance user interfaces. Currently, online stores offer a single-page version to all customers, overlooking individual characteristics. This paper aims to identify the essential components and present a framework for enabling multiple e-commerce user interfaces. It also seeks to address challenges associated with personalized e-commerce user interfaces. The methodology includes detailing the framework for serving diverse e-commerce user interfaces and presenting pilot implementation results. Key components, particularly the role of algorithms in personalizing the user experience, are outlined. The results demonstrate promising outcomes for the implementation of the pilot solution, which caters to various e-commerce user interfaces. User characteristics support multivariant websites, with algorithms facilitating continuous learning. Newly proposed metrics effectively measure changes in user behavior resulting from different interface deployments. This paper underscores the central role of personalized e-commerce user interfaces in optimizing online store efficiency. The framework, supported by machine learning algorithms, showcases the feasibility and benefits of different page versions. The identified components, challenges, and proposed metrics contribute to a comprehensive solution and set the stage for further development of personalized e-commerce interfaces.

UX of AI & PersonalizationResearch Papers

A personalized product recommendation model in e-commerce based on retrieval strategyNguyen · 2024In recent years, online shopping is one of the routine parts in people’s life. It is convenient and takes less effort to purchase it. Regarding the increasing revolution of e-commerce businesses, recommendation engine plays a crucial role in them. Recommendation engines are very popular and easy to implement to their platform nowadays. Due to the extremely high competition of e-commerce businesses, the operation needs to integrate the recommender wisely. This study presents a comprehensive approach to improving user experience and engagement on e-commerce platforms through the implementation of an implicit personalized product recom­ mendation engine. Collaborating with the H&M Group, the research combines the strength of each recom­ mending algorithms which are collaborative filtering, popularity, and Bayesian personalized ranking to develop a robust recommendation system. By leveraging a retrieval strategy that combines multiple algorithmic tech­ niques and evaluating candidates using machine learning models which comprise LightGBM and Deep Neural Network, the study achieves promising results. The authors utilize two popular technical metrics to evaluate their models which are mean average precision at K candidates (MAP@K) and mean average recall at K candidates (MAR@K). The empirical result indicates that the LightGBM model has remarkable performance than Deep Neural Network model, which are 0.06 versus 0.02 respectively in MAP@K and 0.03 versus 0.01 respectively in MAR@K when both recommending ways is at 50 items. Overall, this research contributes a novel framework that addresses the challenges of analyzing large-scale data, cold-start problems, and personalization, thereby enhancing the user experience, and driving sales on e-commerce platforms.

UX of AI & PersonalizationResearch Papers

Inevitable challenges of autonomy: ethical concerns in personalized algorithmic decision-makingWencheng Lu · 2024Breakthroughs in Artificial Intelligence (AI) have transformed human decision-making processes. Individuals increasingly rely on algorithms to assist with various tasks such as searching and shopping. However, the widespread use of personalized algorithmic decisionmaking has raised numerous ethical concerns, specifically its impact on user autonomy. This article examines these concerns and argues that algorithmic decision-making presents several challenges to user autonomy that are difficult to eliminate. These challenges include the fact that algorithms deviate from a user's authentic self, create self-reinforcing loops that narrow the user's self, and lead to a decline in the user's capacities. This article attributes these challenges to the ontological differences between users and AI, as well as their unawareness regarding the ethical risks associated with algorithms, asserting that the sense of autonomy users experience in algorithmic decision-making is merely an illusion. From the perspective of human-AI interaction, it is proposed a strategy that achieves a balance between human insight and AI through a human-in-the-loop approach to mitigate these challenges. This approach maximizes the strengths of AI in processing and analyzing data while ensuring that users retain agency.

UX of AI & PersonalizationResearch Papers

SituationAdapt: Contextual UI Optimization in Mixed Reality with Situation Awareness via LLM ReasoningZhipeng Li, Christoph Gebhardt et al. · 2024Mixed Reality is increasingly used in mobile settings beyond con­ trolled home and ofce spaces. This mobility introduces the need for user interface layouts that adapt to varying contexts. However, existing adaptive systems are designed only for static environments. In this paper, we introduce SituationAdapt, a system that adjusts Mixed Reality UIs to real-world surroundings by considering envi­ ronmental and social cues in shared settings. Our system consists of perception, reasoning, and optimization modules for UI adaptation. Our perception module identifes objects and individuals around the user, while our reasoning module leverages a Vision-and-Language Model to assess the placement of interactive UI elements. This en­ sures that adapted layouts do not obstruct relevant environmental cues or interfere with social norms. Our optimization module then generates Mixed Reality interfaces that account for these consid­ erations as well as temporal constraints. For evaluation, we frst validate our reasoning module’s capability of assessing UI contexts in comparison to human expert users. In an online user study, we then establish SituationAdapt’s capability of producing context- aware layouts for Mixed Reality, where it outperformed previous adaptive layout methods. We conclude with a series of applications and scenarios to demonstrate SituationAdapt’s versatility. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifc permission and/or a fee. Request permissions from permissions@acm.org. UIST ’24, October 13–16, 2024, Pittsburgh, PA, USA © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 979-8-4007-0628-8/24/10 https://doi.org/10.1145/3654777.3676470 CCS CONCEPTS • Human-centered computing → Mixed / augmented reality; Virtual reality; Interactive systems and tools.

UX of AI & PersonalizationResearch Papers

Real-Time Personalized User Interface Adaptation Using Reinforcement LearningAbdulrahman Khamaj, Abdulelah M. Ali · 2024Developing a dynamic, personalized user interface that changes in real-time in response to user behavior is the goal. This paper supplies a modern method to beautify consumers enjoy using Reinforcement Learning (RL) and a Deep Q Network (DQN). Through support examination, the task objectives are to upgrade buyer connections and increment commitment, delight, and undertaking of consummation rates. Users who utilize traditional user interfaces get a common experience because they’re impersonal and unflexible. The potential for higher engagement and happiness levels is limited in the absence of real-time changes based on individual preferences and behaviors. To overcome this problem, the study suggests a cunning technique for a getting-to-comprehend layout that may constantly analyze and modify patron communications. This evaluation is new as it provides a blended RL and DQN framework that modifies person interfaces grade by grade. Dissimilar to conventional methodologies, the proposed form adjusts the utilization of well-known, over-the-top prize moves with the development of the most recent ones through an investigation double-dealing system. EventType, contentId, personId, sensorId, and timestamp are instances of timestamped insights handles that give a thorough skill of client conduct and license planned and nuanced changes.

UX of AI & PersonalizationResearch Papers

Article LIME-Mine: Explainable Machine Learning for User Behavior Analysis in IoT ApplicationsXiaobo Cai · 2024In Internet of Things (IoT) applications, user behavior is influenced by factors such as network structure, user activity, and location. Extracting valuable patterns from user activity traces can lead to the development of smarter, more personalized IoT applications and improved user experience. This paper proposes a LIME-based user behavior preference mining algorithm that leverages Explainable AI (XAI) techniques to interpret user behavior data and extract user preferences. By training a black-box neural network model to predict user behavior using LIME and approximating predictions with a local linear model, we identify key features influencing user behavior. This analysis reveals user behavioral patterns and preferences, such as habits at specific times, locations, and device states. Incorporating user behavioral information into the resource scheduling process, combined with a feedback mechanism, establishes an active discovery network of user demand. Our approach, utilizing edge computing capabilities, continuously fine-tunes and optimizes resource scheduling, actively adapting to user perceptions. Experimental results demonstrate the effectiveness of feedback control in satisfying diverse user resource requests, enhancing user satisfaction, and improving system resource utilization.

UX of AI & PersonalizationResearch Papers

AI-Driven UX/UI Design: Empirical Research and ApplicationsYang Xu · 2024This study explores the transformative impact of AI-driven UX/UI design in the FinTech sector, examining current practices, user preferences, and emerging trends. Through a mixed-methods approach, including surveys, interviews, and case studies, the research reveals significant adoption of AI technologies in FinTech UX/UI design, with 78% of surveyed companies implementing such solutions. Personalization emerges as a dominant trend, with 76% of FinTech apps utilizing AI for tailored user interfaces. The study demonstrates a strong correlation between AI- enhanced features and improved user engagement, with apps incorporating advanced AI features showing a 41% increase in daily active users. Ethical considerations, including data privacy and algorithmic bias, are addressed as critical challenges in AI implementation. The research contributes a conceptual framework for AI-driven UX/UI design in FinTech, synthesizing findings from diverse data sources. Future trends, including emotional AI and augmented reality integration, are explored. The study concludes that while AI- driven UX/UI design offers significant potential for enhancing user experiences in FinTech, balancing innovation with ethical considerations is crucial for responsible implementation and user trust.

UX of AI & PersonalizationResearch Papers

CulturAI: Exploring Mixed Reality Art Exhibitions with Large Language Models for Personalized Immersive ExperiencesNicolas Constantinides · 2024Mixed Reality (MR) technologies have transformed the way in which we interact and engage with digital content, offering immer- sive experiences that blend the physical and virtual worlds. Over the past years, there has been increasing interest in employing Artificial Intelligence (AI) technologies to improve user experience and trustworthiness in cultural contexts. However, the integration of Large Language Models (LLMs) into MR applications within the Cultural Heritage (CH) domain is relatively underexplored. In this work, we present an investigation into the integration of LLMs within MR environments, focusing on the context of virtual art exhi- bitions. We implemented a HoloLens MR application, which enables users to explore artworks while interacting with an LLM through voice. To evaluate the user experience and perceived trustworthi- ness of individuals engaging with an LLM-based virtual art guide, we adopted a between-subject study design, in which participants were randomly assigned to either the LLM-based version or a con- trol group using conventional interaction methods. The LLM-based version allows users to pose inquiries about the artwork displayed, ranging from details about the creator to information about the artwork’s origin and historical significance. This paper presents the technical aspects of integrating LLMs within MR applications and evaluates the user experience and perceived trustworthiness of this approach in enhancing the exploration of virtual art exhibitions. Results of an initial evaluation provide evidence about the positive aspect of integrating LLMs in MR applications. Findings of this work contribute to the advancement of MR technologies for the development of future interactive personalized art experiences. CCS CONCEPTS • Human-centered computing →Mixed / augmented reality; User studies; • Computing methodologies →Artificial intelligence. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). UMAP Adjunct ’24, July 01–04, 2024, Cagliari, Italy © 2024 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-0466-6/24/07 https://doi.org/10.1145/3631700.3664874

UX of AI & PersonalizationResearch Papers

MineXR: Mining Personalized Extended Reality InterfacesHyunsung Cho · 2024Extended Reality (XR) interfaces offer engaging user experiences, but their effective design requires a nuanced understanding of user behavior and preferences. This knowledge is challenging to obtain without the widespread adoption of XR devices. We intro­ duce MineXR, a design mining workflow and data analysis platform for collecting and analyzing personalized XR user interaction and This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License. CHI ’24, May 11–16, 2024, Honolulu, HI, USA © 2024 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-0330-0/24/05 https://doi.org/10.1145/3613904.3642394 experience data. MineXR enables elicitation of personalized in­ terfaces from participants of a data collection: for any particular context, participants create interface elements using application screenshots from their own smartphone, place them in the envi­ ronment, and simultaneously preview the resulting XR layout on a headset. Using MineXR, we contribute a dataset of personalized XR interfaces collected from 31 participants, consisting of 695 XR widgets created from 178 unique applications. We provide insights for XR widget functionalities, categories, clusters, UI element types, and placement. Our open-source tools and data support researchers and designers in developing future XR interfaces. CCS CONCEPTS • Human-centered computing → Mixed / augmented reality; Systems and tools for interaction design. CHI ’24, May 11–16, 2024, Honolulu, HI, USA Cho et al.

UX of AI & PersonalizationResearch Papers

Next-Gen Human-Computer Interaction: A HybridRavindra Changal · 2024In the rapidly evolving landscape of human- computer interaction (HCI), the demand for personalized and adaptive user experiences has grown exponentially. To meet this demand, Research propose a groundbreaking approach leveraging the fusion of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures. This hybrid LSTM-CNN model is designed to enhance adaptability, responsiveness, and user engagement across various interactive platforms.Traditional HCI models often struggle to effectively capture the dynamic nature of user behavior and preferences. By integrating LSTM and CNN, This model achieves a synergistic blend of temporal and spatial feature extraction capabilities. The LSTM component excels in capturing sequential dependencies and long-term patterns, enabling the system to learn from past interactions and anticipate future user actions. Meanwhile, the CNN component efficiently processes spatial information, extracting meaningful features from multimedia inputs such as images, videos, and text.One of the key strengths of proposed model is its adaptability to diverse user contexts and preferences. Through continuous learning and adaptation, it dynamically adjusts its interface, content, and interaction patterns to match the evolving needs and preferences of individual users. Moreover, the hybrid architecture enables real-time processing of multimodal inputs, facilitating seamless interaction across a wide range of devices and platforms.In experimental evaluations, Hybrid LSTM-CNN model demonstrated superior performance compared to baseline methods in terms of user satisfaction, engagement, and task completion rates. Furthermore, it exhibited robustness and scalability, making it suitable for deployment in real-world applications across domains such as e-commerce, entertainment, education, and healthcare.In summary, The proposed approach represents a significant advancement in HCI research, paving the way for next-generation interactive systems that deliver highly adaptive and personalized user experiences.

UX of AI & PersonalizationResearch Papers

Large Language Models Meet User Interfaces: The Case of Provisioning FeedbackStanislav Pozdniakov, Jonathan Brazil et al. · 2024- a *School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD, 4072, Australia* - b *Institute for Teaching and Learning Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia* - c *Centre for Learning Analytics, Faculty of Information Technology, Monash University, Melbourne, VIC, 3800, Australia* - d *School of Computer Science, University of Auckland, 38 Princes Street Auckland, 1010, New Zealand*

UX of AI & PersonalizationResearch Papers

An empirical study of AI techniques in mobile applications ✩Haoye Tian b, Zhijie Wang c et al. · 2024The integration of artificial intelligence (AI) into mobile applications has significantly transformed various domains, enhancing user experiences and providing personalized services through advanced machine learning (ML) and deep learning (DL) technologies. AI-driven mobile apps typically refer to applications that leverage ML/DL technologies to perform key tasks such as image recognition and natural language processing. Despite existing research exploring how mobile apps exploit AI techniques, they have the following main limitations: (1) Most existing studies focus on DL-based apps, with limited research on ML-based apps. (2) Existing research typically focuses on investigating the apps and the technologies utilized in the apps, lacking user-level analysis. (3) The number of apps studied is limited, with only 1,000 to 2,000 ML/DL apps identified after filtering. To fill the gap, in this paper, we conducted the most extensive empirical study on AI applications, exploring on-device ML apps, on-device DL apps, and AI service-supported (cloud-based) apps. Our study encompasses 56,682 real- world AI applications, focusing on three crucial perspectives: (1) Application analysis, where we analyze the popularity of AI apps and investigate the update states of AI apps; (2) Framework and model analysis, where we analyze AI framework usage and AI model protection; (3) User analysis, where we examine user privacy protection and user review attitudes. Our study has strong implications for AI app developers, users, and AI R&D. On one hand, our findings highlight the growing trend of AI integration in mobile applications, demonstrating the widespread adoption of various AI frameworks and models. On the other hand, our findings emphasize the need for robust model protection to enhance app security. Additionally, our study highlights the importance of user privacy and presents user attitudes towards the AI technologies utilized in current AI apps. We provide our AI app dataset (currently the most extensive AI app dataset) as an open-source resource for future research on AI technologies utilized in mobile applications.

UX of AI & PersonalizationResearch Papers

“I know even if you don’t tell me”: Understanding Users’ Privacy Preferences Regarding AI-based Inferences of Sensitive Information for PersonalizationSumit Asthan · 2024Personalization improves user experience by tailoring interactions relevant to each user’s background and preferences. However, per­ sonalization requires information about users that platforms often collect without their awareness or their enthusiastic consent. Here, we study how the transparency of AI inferences on users’ personal data afects their privacy decisions and sentiments when sharing data for personalization. We conducted two experiments where participants (N=877) answered questions about themselves for per­ sonalized public arts recommendations. Participants indicated their consent to let the system use their inferred data and explicitly pro­ vided data after awareness of inferences. Our results show that participants chose restrictive consent decisions for sensitive and incorrect inferences about them and for their answers that led to such inferences. Our fndings expand existing privacy discourse to inferences and inform future directions for shaping existing consent mechanisms in light of increasingly pervasive AI inferences. CCS CONCEPTS • Security and privacy → Social aspects of security and pri­ vacy; • Human-centered computing → Empirical studies in HCI.

UX of AI & PersonalizationResearch Papers

One Size Does Not Fit All: Multivariant User Interface Personalization in E-CommerceAdam Wasilewski · 2024One of the most visible manifestations of the changes brought about by the digitization of everyday life is undoubtedly the spread of electronic commerce. It is difficult to think of the digital economy without considering transactions through electronic channels. In turn, the user interface (UI) is the key to e- commerce, as it is usually the first and primary point of contact between business and consumer. A key trend in e-commerce is the personalization of communications, which can improve the user experience, increase customer satisfaction and deliver tangible business benefits. Today, it is technically possible to base this personalization on an analysis of user behavior using artificial intelligence and machine learning techniques. A common form of personalization in e-commerce is the use of product recommendation systems, but the user interface can be tailored much more extensively. The approach described and discussed in this paper is a multivariant user interface that allows the layout to be tailored to the characteristics, attributes, and behaviors of customer groups generated using machine learning techniques. The results of the research carried out make it possible to verify the practicality of the proposed solution and provide an opportunity to identify development directions that take into account the potential of artificial intelligence. The application of the concept described in the paper is broad, covering all aspects of e-commerce design that require compromises when serving a single UI variant, but allow flexibility and customization for different users when serving a multivariant UI. INDEX TERMS Artificial intelligence, e-commerce, machine learning, personalization, user interface.

UX of AI & PersonalizationResearch Papers

A Systematic Review of the Impact of Auxiliary Information on Recommender SystemsMatthew Ayemowa · 2024Recommender systems are essential tools that provide personalized user experiences across various domains such as e-commerce, entertainment, social media, education and content streaming. The integration of auxiliary information, including user demographics, item attributes, and contextual data has shown significant promise in enhancing the performance of recommender systems. This systematic review investigates the impact of incorporating auxiliary information into various types of recommender systems, examining recent advancements, methodologies, datasets, evaluation metrics, and to equally examine its significance on generative artificial intelligence. Similarly, five (5) reputable online databases were used to identify the relevant studies for answering our research questions. To obtain effective results of our findings, we focus more on the recent studies published between (2019 - June 2024) to ensure that of our findings up-to-date. After filtering the selected primary papers that solely focused on auxiliary information recommender systems a total of 37 papers were identified and analyzed. Our analysis shows the most utilized datasets, metrics, models, addressed issues and future works. Research limitations and future scope are also highlighted to assist researchers and practitioners for their future studies. INDEX TERMS Recommender systems, auxiliary information, data sparsity, cold start problem.

UX of AI & PersonalizationResearch Papers

Clustering Methods for Adaptive e-Commerce User InterfacesAdam Wasilewski · 2023Typical online shops have one interface provided to all users, regardless of their use of the shop. Meanwhile, user behavior varies and therefore different interfaces could be provided to different user groups. Various methods can be used to cluster users, including those using artificial intelligence (AI) methods. AI-based personalization allows e- commerce businesses to provide tailored recommendations to each indi- vidual customer based on preferences, purchase history, and behavior on the website. This article presents a study of the impact of an AI- based clustering method on the effectiveness of a dedicated user inter- face implemented and delivered to the customers of an e-shop. The first study included five methods, and two of them - agglomerative clustering and K-means clustering - were selected for detailed analysis. For both of these methods, an in-depth research was carried out and the impact of the clustering method on the quality of user clusters, as measured by the effectiveness of the dedicated interface in relation to the effectiveness of the default interface, was verified.

UX of AI & PersonalizationResearch Papers

Emoticontrol : Emotions-based Control of User-Interfaces AdaptationsKARTHIK VAIDHYANATHAN, IIIT Hyderaba · 2023Emotions are integral to human nature, and their existence, duration, and evolution could lead to specific behaviors. If emotions and behaviors are ignored in the design of socio-technical systems, they will fail or cause discomfort. User interfaces (UIs) are elements of interactive systems able to trigger or moderate emotions. UIs are increasingly designed adaptive to users' various characteristics, intending to improve their satisfaction, performance, and decisions. However, previous adaptation supervising approaches are not effectively adopted in real life since they neglect the dynamic behaviors of humans or systems. This paper proposes Emoticontrol, a quality-driven approach to adapting UIs to users' emotions using Model-Free Reinforcement Learning (MFRL). The approach aims to maximize applying the essential adaptations and minimize the unnecessary ones towards users' enhanced quality of experience (QoE). The approach also considers improving the software quality of service (QoS) by designing software architecture alternatives. We chose emergency evacuation training as a suitable evaluation domain since people experience intense emotions in potential danger. We performed experiments with a mobile application we developed that acts as a recommender system in evacuation training. By taking contextual input of the users' basic emotions from face recognition, the application intelligently adapts its UI to quickly lead people to safe areas while keeping them emotionally controlled. We consider software performance a crucial QoS; thus, we adopt and test architectures that facilitate an acceptable level of performance. The evaluation process confirms the efficiency and effectiveness of the MFRL in iterations, as well as compared to other UI adaptation techniques.

UX of AI & PersonalizationResearch Papers

Human-Centered Artificial Intelligence: Designing for User Empowerment and Ethical ConsiderationsUsman Ahmad Usmani · 2023Human-Centered Artificial Intelligence (AI) focuses on AI systems prioritizing user empowerment and ethical considerations. We explore the importance of user- centric design principles and ethical guidelines in creating AI technologies that enhance user experiences and align with human values. It emphasizes user empowerment through personalized experiences and explainable AI, fostering trust and user agency. Ethical considerations, including fairness, transparency, accountability, and privacy protection, are addressed to ensure AI systems respect human rights and avoid biases. Effective human AI collaboration is emphasized, promoting shared decision-making and user control. By involving interdisciplinary collaboration, this research contributes to advancing human-centered AI, providing practical recommendations for designing AI systems that enhance user experiences, promote user empowerment, and adhere to ethical standards. It emphasizes the harmonious coexistence between humans and AI, enhancing well-being and autonomy and creating a future where AI technologies benefit humanity. Overall, this research highlights the significance of human-centered AI in creating a positive impact. By centering on users' needs and values, AI systems can be designed to empower individuals and enhance their experiences. Ethical considerations are crucial to ensure fairness and transparency. With effective collaboration between humans and AI, we can harness the potential of AI to create a future that aligns with human aspirations and promotes societal well-being.

UX of AI & PersonalizationResearch Papers

Adaptive user interface for workflow-ERP systemMarcin Smereka, Grzegorz Kołaczek et al. · 2023In this paper, the problem of user interface recommendations for workflow management systems is investigated. The user interface is automatically adapted using a software tool based on content-based filtering. This tool collects information about the way processes are carried out in an organization, analyzes and processes the data, and recommends the next action to the user in order to increase efficiency, facilitate training, and improve decision-making. The proposed tool was verified in the real environment within three organizations. For each organization, after at least several weeks of learning, the tool was able to offer suggestions that were selected by real users. 2382 Marcin Smereka et al. / Procedia Computer Science 225 (2023) 2381–2391 The aim of the project carried out by Sente was to investigate the possibility of automatically analyzing user behavior in business software (especially ERP class) in order to automatically adapt the way processes are carried out to these behaviors using content-based filtering algorithms. A tool that would be able to collect information about the way processes are carried out in an organization, analyze and process the collected data and recommend the next action to the user could increase the efficiency of using the ERP system, and facilitate training and decision-making processes. 2. State of the art According to Kobsa [7], proper user model selection should be the starting point when designing the user inter- face. However, with the growing number of potential users, particularly in web-based systems, the differences among them are also increasing. Consequently, the user model is becoming more complex, resulting in a highly differentiated proposed user interface, such as in interaction styles. [14]. The user differences may reflex their demographical, psy- chological, sociological, and anthropological user characteristics, which have an influence on the users’ information needs, interaction habits, and potential limitations of the interaction systems usage. The consequence of these differ- ences is causing difficulties in modeling these users in the standard way [7], in a result more sophisticated solutions have been proposed, for example, one enhanced with the application of ontologies [12]. The ontologies may be used to enhance information systems design and development on at least several different levels, such as database integration, business logic, or Graphical User Interfaces (GUI) [8]. User interfaces have been also ado

UX of AI & PersonalizationResearch Papers

From Sci-Fi to Reality: The Evolution of Human-Computer Interaction with ArtificialAstha Sharm · 2023Artificial intelligence (AI) has transformed human computer interaction (HCI) by providing unprecedented levels of personalization and adaptivity. This research study discusses about the potential of AI in HCI, including advanced natural language processing, seamless integration with Internet of Things (IoT) devices, and the use of emotion recognition. However, the increasing utilization of AI result in various challenges in terms of data privacy, bias, and job displacement. As such, it is essential for researchers and developers to prioritize ethical, transparent, and accessible AI-powered systems. The impact of AI on HCI is far-reaching and transformative, and it will continue to shape the future of technology and Human Computer Interaction (HCI).

UX of AI & PersonalizationResearch Papers

User-Driven Constraints for Layout Optimisation in Augmented RealityAziz Niyazov · 2023Automatic layout optimisation allows users to arrange augmented reality content in the real-world environment without the need for tedious manual interactions. This optimisation is often based on modelling the intended content placement as constraints, defined as cost functions. Then, applying a cost minimization algorithm leads to a desirable placement. However, such an approach is lim- ited by the lack of user control over the optimisation results. In this paper we explore the concept of user-driven constraints for augmented reality layout optimisation. With our approach users can define and set up their own constraints directly within the real-world environment. We first present a design space composed of three dimensions: the constraints, the regions of interest and the constraint parameters. Then we explore which input gestures can be employed to define the user-driven constraints of our design space through a user elicitation study. Using the results of the study, we propose a holistic system design and implementation demon- strating our user-driven constraints, which we evaluate in a final user study where participants had to create several constraints at the same time to arrange a set of virtual contents. CCS CONCEPTS • Human-centered computing →Interactive systems and tools. ACM Reference Format: Aziz Niyazov, Barrett Ens, Kadek Ananta Satriadi, Nicolas Mellado, Loïc Barthe, Tim Dwyer, and Marcos Serrano. 2023. User-Driven Constraints Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. CHI ’23, April 23–28, 2023, Hamburg, Germany © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 978-1-4503-9421-5/23/04...$15.00 https://doi.org/10.1145/3544548.3580873 for Layout Optimisation in Augmented Reality. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23), April 23–28, 2023, Hamburg, Germany. ACM, New York, NY, USA, 16 pages. https: //doi.org/10.1145/3544

UX of AI & PersonalizationResearch Papers

User Interface and Architecture Adaption Based onMahyar T. Moghaddam∗, Mina Alipour et al. · 2023This paper shows how emotions and behavior considerations in socio-technical systems lead to high-quality self-adaptations, both at application and architecture levels. In our approach, an interactive control system assesses the reconfigurations that enhance the quality of service (QoS) while considering humans’ quality of experience (QoE). We use a Model-Free Reinforcement Learning (MFRL) approach to self- adapt user interfaces (UIs) to users’ emotions. The approach aims to maximize applying the essential adaptations and minimize the unnecessary ones towards users’ QoE, i.e., task comple- tion and satisfaction. If the control system detects a drop in QoS in emotion-based adaptations or other functions, another level of adaptation reconfigures the architecture towards better quality. We chose emergency evacuation training as a suitable evaluation domain since people experience intense emotions in such potential danger. We performed experiments with a mobile application we developed that acts as a recommender system in emergency training. By taking contextual input of the users’ basic emotions from face recognition and their mobility behavior, the application intelligently adapts its UI to quickly lead people to safe areas while keeping them emotionally stable. In addition to UI adaptation, the system is capable of architecture-level adaptations to decrease response time if required. The evaluation process confirms the efficiency of the MFRL in iterations, as well as compared to other possible UI adaptation techniques. The emerging results also show that architecture-level adaptations positively impact the system performance and users’ emotions and performance. Index Terms—Software Architecture, Emotions, Behaviors, Reinforcement Learning, User Interface, Emergency.

UX of AI & PersonalizationResearch Papers

Personalizing News Recommendtions for Older people with their Life StoriesQin Gao · 2023Editorial Board Members Elisa Bertino, Purdue University, West Lafayette, IN, USA Wen Gao, Peking University, Beijing, China Bernhard Steffen , TU Dortmund University, Dortmund, Germany Moti Yung , Columbia University, New York, NY, USA The series Lecture Notes in Computer Science (LNCS), including its subseries Lecture Notes in Artificial Intelligence (LNAI) and Lecture Notes in Bioinformatics (LNBI), has established itself as a medium for the publication of new developments in computer science and information technology research, teaching, and education. LNCS enjoys close cooperation with the computer science R & D community, the series counts many renowned academics among its volume editors and paper authors, and collaborates with prestigious societies. Its mission is to serve this international commu- nity by providing an invaluable service, mainly focused on the publication of conference and workshop proceedings and postproceedings. LNCS commenced publication in 1973. Qin Gao · Jia Zhou · Vincent G. Duffy · Margherita Antona · Constantine Stephanidis Editors

UX of AI & PersonalizationResearch Papers

Choice-based Personalization in MOOCs: Impact on Activity and Perceived ValueIlana Ram · 2023Personalization in education describes instruction that is tailored to learners’ inter­ ests, attributes, or background and can be applied in various ways, one of which is through choice. In choice-based personalization, learners choose topics or resources that fit them the most. Personalization may be especially important (and under- used) with diverse learners, such as in a MOOC context. We report the impact of choice-based personalization on activity level, learning gains, and satisfaction in a Climate Science MOOC. The MOOC’s learning assignments had learners choose resources on climate-related issues in either their geographic locale (Personalized group) or in given regions (Generic group). 219 learners completed at least one of the two assignments. Over the entire course, personalization increased learn­ ers’ activity (number of course events), self-reported understanding of local issues, and self-reported likelihood to change climate-related habits. We found no differ­ ences on assignment completion rate, assignment length, and self-reported time-on- task. These results show that benefits of personalization extend beyond the original task and affect learners’ overall experience. We discuss design and implications of choice-based personalization, as well as opportunities for choice-based personaliza­ tion at scale.

UX of AI & PersonalizationResearch Papers

Impact of AI on Mobile Computing - A Systematic Review from a Human Factors PerspectiveHelmut Degen · 2023Editorial Board Members Elisa Bertino, Purdue University, West Lafayette, IN, USA Wen Gao, Peking University, Beijing, China Bernhard Steffen , TU Dortmund University, Dortmund, Germany Moti Yung , Columbia University, New York, NY, USA The series Lecture Notes in Computer Science (LNCS), including its subseries Lecture Notes in Artificial Intelligence (LNAI) and Lecture Notes in Bioinformatics (LNBI), has established itself as a medium for the publication of new developments in computer science and information technology research, teaching, and education. LNCS enjoys close cooperation with the computer science R & D community, the series counts many renowned academics among its volume editors and paper authors, and collaborates with prestigious societies. Its mission is to serve this international commu- nity by providing an invaluable service, mainly focused on the publication of conference and workshop proceedings and postproceedings. LNCS commenced publication in 1973. Helmut Degen · Stavroula Ntoa · Abbas Moallem Editors

UX of AI & PersonalizationResearch Papers

InteractionAdapt: Interaction-driven Workspace Adaptation for Situated Virtual Reality EnvironmentsYi Fei Cheng · 2023Virtual Reality (VR) has the potential to transform how we work: it enables fexible and personalized workspaces beyond what is possi­ ble in the physical world. However, while most VR applications are designed to operate in a single empty physical space, work environ­ ments are often populated with real-world objects and increasingly diverse due to the growing amount of work in mobile scenarios. In this paper, we present InteractionAdapt, an optimization-based method for adapting VR workspaces for situated use in varying everyday physical environments, allowing VR users to transition between real-world settings while retaining most of their personal­ ized VR environment for efcient interaction to ensure temporal consistency and visibility. InteractionAdapt leverages physical afor­ dances in the real world to optimize UI elements for the respectively most suitable input technique, including on-surface touch, mid-air touch and pinch, and cursor control. Our optimization term thereby models the trade-of across these interaction techniques based on Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifc permission and/or a fee. Request permissions from permissions@acm.org. UIST ’23, October 29–November 01, 2023, San Francisco, CA, USA © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 979-8-4007-0132-0/23/10...$15.00 https://doi.org/10.1145/3586183.3606717 experimental fndings of 3D interaction in situated physical envi­ ronments. Our two evaluations of InteractionAdapt in a selection task and a travel planning task established its capability of support­ ing efcient interaction, during which it produced adapted layouts that participants preferred to several baselines. We further show­ case the versatility of our approach through applications that cover a wide range of use cases. CCS CONCEPTS • Human-centered computing → Mixed / augmented reality; Virtual reality; User interface management systems.

UX of AI & PersonalizationResearch Papers

Algorithms for the Development of Adaptive Web Interfaces: A Systematic Literature ReviewMichael Rodrigues da Silva · 2022The User Interface (UI) is a fundamental part of web interfaces, being considered a fundamental aspect of user satisfaction when using a web application. The UI that offers a good User Experience (UX) is the one that makes the interaction as simple and efficient as possible for the user to achieve the previously desired goals. In this way, web interfaces that have adaptive interfaces tend to offer a better UX, as they seek to adapt to the needs and desires of users. Therefore, this paper carries out a Systematic Literature Review (SLR) to identify the most used algorithms for the development of Adaptive Web Interfaces (AWI). The results indicate that the use of Machine Learning (ML) techniques, both to improve the performance of some task and to induce results, are the most popular ways to develop AWI. During the execution of SLR, 294 articles were analyzed using automated and manual searches, and inclusion, exclusion and quality criteria were applied to them. The research findings allow us to state that the topic is quite relevant to the academic community, with an average publication of 20 papers each year.1

UX of AI & PersonalizationResearch Papers

Personalized recommendation model of electronic commerce in new media era based on semantic emotion analysis2022© 2022 Liu and Ding. This is an open-access article distributed under the terms of the Creative Commons Attribution License \(CC BY\). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

UX of AI & PersonalizationResearch Papers

ScalAR: Authoring Semantically Adaptive Augmented Reality Experiences in Virtual RealityXun Qian · 2022Augmented Reality (AR) experiences tightly associate virtual con- tents with environmental entities. However, the dissimilarity of different environments limits the adaptive AR content behaviors under large-scale deployment. We propose ScalAR, an integrated workflow enabling designers to author semantically adaptive AR ex- periences in Virtual Reality (VR). First, potential AR consumers col- lect local scenes with a semantic understanding technique. ScalAR then synthesizes numerous similar scenes. In VR, a designer au- thors the AR contents’ semantic associations and validates the design while being immersed in the provided scenes. We adopt a decision-tree-based algorithm to fit the designer’s demonstrations as a semantic adaptation model to deploy the authored AR expe- rience in a physical scene. We further showcase two application scenarios authored by ScalAR and conduct a two-session user study where the quantitative results prove the accuracy of the AR content rendering and the qualitative results show the usability of ScalAR. This work is licensed under a Creative Commons Attribution International 4.0 License. CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA © 2022 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-9157-3/22/04. https://doi.org/10.1145/3491102.3517665 CCS CONCEPTS • Human-centered computing →Mixed / augmented reality; Virtual reality; Interactive systems and tools.

UX of AI & PersonalizationResearch Papers

User acceptance on content optimization algorithms: predicting filter bubbles in conversational AI servicesLONG PAPER · 2022As the role of artificial intelligence (AI) agents in information curation has emerged with recent advancements in AI tech- nologies, the present study explored which users would potentially be susceptible to the filter bubble phenomenon. First, a large-scale analysis of conversational agent users in South Korea (N = 2808) was conducted to investigate the relative importance of content optimization algorithms in shaping positive user experience. Five user clusters were identified based on their information technology proficiency and demographics, and a multiple-group path analysis was performed to com- pare the influences of content optimization algorithms across the user groups. The results indicated that the personalization algorithm generally exhibited a stronger impact on evaluations of an AI agent’s usefulness than the diversity algorithm. In addition, increased user age and greater Internet usage were found to decrease the importance of objectivity in shaping trust in AI agents. This study improves the understanding of the social influence of AI technology and suggests the necessity of segmented approaches in the development of AI technology.

UX of AI & PersonalizationResearch Papers

Personalized User Interface Elements Recommendation SystemHao Liu, Xiangxian Li et al. · 2022This paper introduces a personalized user interface element recom- mendation system, in which the model can recommend personalized user inter- face elements by introducing user features and user evaluations in the offline training. Through experiments, we found that compared with common machine learning algorithms, the Field-aware Factorization Machine that introduced user feature intersections has achieved a better accuracy in the recommendation, which shows the advantages of introducing user features and feature intersections in the recommendation of interface elements.

UX of AI & PersonalizationResearch Papers

Real-Time Adaptation of Context-Aware Intelligent User Interfaces, for Enhanced Situational AwarenessZinova Stefanidi, George Margetis · 2022In this work, a novel computational approach for the dynamic adaptation of User Inter- faces (UIs) is proposed, which aims at enhancing the Situational Awareness (SA) of users by leveraging the current context and providing the most useful information, in an optimal and efficient manner. By combining Ontology modeling and reasoning with Combinatorial Optimization, the system decides what information to present, when to present it, where to visualize it in the display - and how, taking into consideration contextual factors as well as placement constraints. The main objective of the proposed approach is to optimize the SA associated with the displayed UI at run-time, while avoiding information overload and induced stress. In the context of this work, we have deployed our computational approach to the use case of an Augmented Reality (AR) system for Law Enforcement Agents (LEAs). To explore the benefits and limitations of the developed system, two evaluations have been conducted. The first one was an expert-based evaluation with LEAs and User Experience (UX) experts, assessing the appropriateness of the system’s decisions. The second one was a user-based evaluation involving LEAs from different agencies, estimating the SA, the mental workload and the overall UX associated with the system, through an AR simulation. The results indicate that the system enhances SA, and while not imposing workload, it provides an overall positive UX. INDEX TERMS Adaptive user interfaces, augmented reality, context-awareness, intelligent user interfaces, ontology modeling, ontology reasoning, situational awareness, user interface optimization.

UX of AI & PersonalizationResearch Papers

Explaining User Models with Different Levels of Detail for Transparent Recommendation: A User StudyMouadh Guesmi · 2022In this paper, we shed light on explaining user models for trans- parent recommendation while considering user personal character- istics. To this end, we developed a transparent Recommendation and Interest Modeling Application (RIMA) that provides interactive, layered explanations of the user model with three levels of detail (basic, intermediate, advanced) to meet the demands of different types of end-users. We conducted a within-subject study (N=31) to investigate the relationship between personal characteristics and the explanation level of detail, and the effects of these two variables on the perception of the explainable recommender system with regard to different explanation goals. Based on the study results, we provided some suggestions to support the effective design of user model explanations for transparent recommendation. CCS CONCEPTS • Human-centered computing →Interactive systems and tools; • Computing methodologies →Artificial intelligence.

UX of AI & PersonalizationResearch Papers

How Well Does the Algorithm Know Me?Julia Graefe · 2022The use of artificial intelligence (AI) in everyday products increases and thereby also the interest on human-AI interaction. Research on user-adaptive systems still lacks uniform terminology and common definitions. To fill this gap, a taxonomy of user-adaptive systems, the Levels of Adaptive Sensitive Responses (LASR), has been proposed in previous research. The LASR levels are a first step towards a uniform classification of user-adaptive systems, but still need to be validated in terms of comprehensiveness and accuracy. This paper presents an evaluation of the LASR taxonomy through its application on technical products, to examine whether the taxonomy is complete and applicable in a meaningful way. We used a four steps analysis approach, covering the definition of classification criteria, two research iterations on suitable user-adaptive product features, and the analysis to which degree these features fulfill the classification criteria. As a result, our analysis shows that nine out of eleven analyzed product features can clearly be categorized into one of the five levels of LASR. Two features showed characteristics of two different levels which reveals that the levels are not mutually exclusive but can be combined within one product feature.

UX of AI & PersonalizationResearch Papers

AUIT – the Adaptive User Interfaces Toolkit for Designing XR ApplicationsAnna Maria Feit · 2022Adaptive user interfaces can improve experiences in Extended Re­ ality (XR) applications by adapting interface elements according to the user’s context. Although extensive work explores diferent adaptation policies, XR creators often struggle with their imple­ mentation, which involves laborious manual scripting. The few available tools are underdeveloped for realistic XR settings where it is often necessary to consider conficting aspects that afect an adap­ tation. We fll this gap by presenting AUIT, a toolkit that facilitates the design of optimization-based adaptation policies. AUIT allows creators to fexibly combine policies that address common objec­ tives in XR applications, such as element reachability, visibility, and consistency. Instead of using rules or scripts, specifying adaptation policies via adaptation objectives simplifes the design process and This work is licensed under a Creative Commons Attribution International 4.0 License. UIST ’22, October 29-November 2, 2022, Bend, OR, USA © 2022 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-9320-1/22/10. https://doi.org/10.1145/3526113.3545651 enables creative exploration of adaptations. After creators decide which adaptation objectives to use, a multi-objective solver fnds appropriate adaptations in real-time. A study showed that AUIT allowed creators of XR applications to quickly and easily create high-quality adaptations. CCS CONCEPTS • Human-centered computing → Systems and tools for in­ teraction design; Gestural input; Mixed / augmented reality; Virtual reality; User interface toolkits.

UX of AI & PersonalizationResearch Papers

Service-Aware Personalized Item RecommendationReceived February · 2022Current recommender systems employ item-centric properties to estimate ratings and present the results to the user. However, recent studies highlight the fact that the stages of item fruition also involve extrinsic factors, such as the interaction with the service provider before, during and after item selection. In other words, a holistic view of consumer experience, including local properties of items, as well as consumers’ perceptions of item fruition, should be adopted to enhance user awareness and decision-making. In this work, we integrate recommender systems with service models to reason about the different stages of item fruition. By exploiting the Service Journey Maps to define service-based item and user profiles, we develop a novel family of recommender systems that evaluate items by taking preference management and overall consumer experience into account. Moreover, we introduce a two-level visual model to provide users with different information about recommendation results: (i) the higher level summarizes consumer experience about items and supports the identification of promising suggestions within a possibly long list of results; (ii) the lower level enables the exploration of detailed data about the local properties of items. In a user test instantiated in the home-booking domain, we compared our models to standard recommender systems. We found that the service-based algorithms that only use item fruition experience excel in ranking and minimize the error in rating estimation. Moreover, the combination of data about item fruition experience and item properties achieves slightly lower recommendation performance; however, it enhances users’ perceptions of the awareness and the decision-making support provided by the system. These results encourage the adoption of service-based models to summarize user preferences and experience in recommender systems. INDEX TERMS Information filtering, recommender systems, data visualization, service modeling.

UX of AI & PersonalizationResearch Papers

User Feedback Design in AI-Driven Mood Tracker Mobile AppsHsi Yuan Chu, Yvette Shen(B) · 2022AI-driven civilian healthcare systems can offer personalized mental health care to more people. When users provide feedback to the system, they can influence the results of the AI models and improve their user experiences over time. In this study, we investigate feedback designs in mood tracker apps and identify feedback implementation opportunities through the lens of user expe- rience and user interface design. We reviewed the feedback designs on popular mood tracker apps and discovered that the feedback mechanisms were not reg- ularly implemented in all stages of tracking moods. To explore feedback design opportunities in line with user actions, user mindsets, and user emotions, a journey map was developed to visualize the interaction with a mood tracker app from the user perspective. Based on the journey map, a mood tracker app prototype was created for the purpose of user testing. The preliminary user testing results vali- dated some feedback design recommendations that highlight the human-centered design approach of crafting user experiences that leverage AI.

UX of AI & PersonalizationResearch Papers

Model-based intelligent user interface adaptation: challenges and future directionsSilvia Abrahao · 2021Adapting the user interface of a software system to the requirements of the context of use continues to be a major challenge, particularly when users become more demanding in terms of adaptation quality. A considerable number of methods have, over the past three decades, provided some form of modelling with which to support user interface adaptation. There is, however, a crucial issue as regards in analysing the concepts, the underlying knowledge, and the user experience afforded by these methods as regards comparing their benefits and shortcomings. These methods are so numerous that positioning a new method in the state of the art is challenging. This paper, therefore, defines a conceptual reference framework for intelligent user interface adaptation containing a set of conceptual adaptation properties that are useful for model-based user interface adaptation. The objective of this set of properties is to understand any method, to compare various methods and to generate new ideas for adaptation. We also analyse the opportunities that machine learning techniques could provide for data processing and analysis in this context, and identify some open challenges in order to guarantee an appropriate user experience for end-users. The relevant literature and our experience in research and industrial collaboration have been used as the basis on which to propose future directions in which these challenges can be addressed.

UX of AI & PersonalizationResearch Papers

Adapting User Interfaces with Model-based Reinforcement LearningKashyap Todi · 2021Adapting an interface requires taking into account both the positive and negative efects that changes may have on the user. A carelessly picked adaptation may impose high costs to the user – for example, due to surprise or relearning efort – or “trap” the process to a suboptimal design immaturely. However, efects on users are hard to predict as they depend on factors that are latent and evolve over the course of interaction. We propose a novel approach for adaptive user interfaces that yields a conservative adaptation policy: It fnds benefcial changes when there are such and avoids changes when there are none. Our model-based reinforcement learning Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifc permission and/or a fee. Request permissions from permissions@acm.org. CHI ’21, May 8–13, 2021, Yokohama, Japan © 2021 Association for Computing Machinery. ACM ISBN 978-1-4503-8096-6/21/05...$15.00 https://doi.org/10.1145/3411764.3445497 method plans sequences of adaptations and consults predictive HCI models to estimate their efects. We present empirical and simulation results from the case of adaptive menus, showing that the method outperforms both a non-adaptive and a frequency-based policy. CCS CONCEPTS • Human-centered computing → Interactive systems and tools.

UX of AI & PersonalizationResearch Papers

Multimodal Differentiation of Obstacles in Repeated Adaptive Human-Computer InteractionsFelix Putze · 2021Human Computer Interaction can be impeded by various interac- tion obstacles, impacting a user’s perception or cognition. In this work, we detect and discriminate such interaction obstacles from different data modalities to compensate for them through User Interface (UI) adaptation. For example, we detect memory-based obstacles from brain activity and compensate through repetition of information in the UI; we detect visual obstacles from user behavior and compensate by complementing visual with auditory informa- tion in the UI. Online cognitive adaptive systems should be able to decide the most suitable UI adaptation given inputs from several obstacles detectors. In this paper, we employ a Bayesian fusion approach upon different underlying obstacles detectors over mul- tiple consecutive interaction sessions. Experimental results show that the model promisingly outperforms the baseline in the first interaction with an average accuracy of 72.5% and further improves drastically in subsequent interactions with additional information, with an average accuracy of 98%. CCS CONCEPTS • Human-centered computing →User models.

UX of AI & PersonalizationResearch Papers

X5Learn: A Personalised Learning Companion at the Intersection of AI and HCIMaría Pérez-Ortiz, Claire Dormann et al. · 2021X5Learn (available at https://x5learn.org) is a human-centered AI- powered platform for supporting access to free online educational resources. X5Learn provides users with a number of educational tools for interacting with open educational videos, and a set of tools adapted to suit the pedagogical preferences of users. It is intended to support both teachers and students, alike. For teachers, it provides a powerful platform to reuse, revise, remix, and redistribute open courseware produced by others. These can be videos, pdfs, exercises and other online material. For students, it provides a scaffolded and informative interface to select content to watch, read, make notes and write reviews, as well as a powerful personalised recommenda- tion system that can optimise learning paths and adjust to the user’s learning preferences. What makes X5Learn stand out from other educational platforms, is how it combines human-centered design with AI algorithms and software tools with the goal of making it intuitive and easy to use, as well as making the AI transparent to the user. We present the core search tool of X5Learn, intended to support exploring open educational materials. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). IUI ’21 Companion, April 14–17, 2021, College Station, TX, USA © 2021 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-8018-8/21/04. https://doi.org/10.1145/3397482.3450721 CCS CONCEPTS • Information systems →Users and interactive retrieval; Per- sonalization; Recommender systems; Search interfaces; • Ap- plied computing →Interactive learning environments.

UX of AI & PersonalizationResearch Papers

Artificial intelligence in E‑Commerce: a bibliometric study and literature reviewBawack · 2021This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes guidelines on how information systems (IS) research could contribute to this research stream. To this end, the innovative approach of combining bibliometric analysis with an extensive literature review was used. Bibliometric data from 4335 documents were analysed, and 229 articles published in leading IS journals were reviewed. The bibliometric analysis revealed that research on AI in e-commerce focuses primarily on recommender systems. Sentiment analysis, trust, personalisation, and optimisation were identified as the core research themes. It also places China- based institutions as leaders in this researcher area. Also, most research papers on AI in e-commerce were published in computer science, AI, business, and management outlets. The literature review reveals the main research topics, styles and themes that have been of interest to IS scholars. Proposals for future research are made based on these findings. This paper presents the first study that attempts to synthesise research on AI in e-commerce. For researchers, it contributes ideas to the way forward in this research area. To practitioners, it provides an organised source of information on how AI can support their e-commerce endeavours.

UX of AI & PersonalizationResearch Papers

Editorial: Psychological Models for Personalized Human-Computer Interaction (HCI)Anton Nijholt · 2021The behavior of users in the digital world, such as online shopping or social media activity, is increasingly supported by personalized systems, such as recommender systems \(Ricci et al., 2015\) and personalized learning. Early work on personalized systems was mainly data-driven, based on behavioral data, such as ratings, likes, and purchases (e.g., Bell et al., 2007\). Although these systems are useful for both users and service providers, the main downside is the limited interpretability and explainability of the data. Such limitations in both interpretability and explainability translate in using data without understanding the root-cause of behaviors. Recent work has thus started to adopt a more theory-driven approach by including psychological theories and models to improve personalized systems (see for an overview; Graus and Ferwerda, 2019\). These systems take advantage of psychological theories/models, such as emotions (Tkalci ˇ c et al., 2013b; Tkal ˇ ci ˇ c and ˇ Ferwerda, 2018\), personality \(Ferwerda et al., 2017; Wu et al., 2018\), skills \(Ferwerda and Graus, 2018\), and culture \(Schedl et al., 2017\) to explain and predict behaviors of users. This allows for a deeper understanding of users' behavior, preferences, and needs, which in turn also lead to more generalizable results.

UX of AI & PersonalizationResearch Papers

User Context Ontology for Adaptive Mobile-Phone InterfacesReceived June · 2021The Adaptive User Interface (AUI) adapts to the changes in the context of use and provides improved interaction abilities for different users. The adaptivity in the user interfaces requires in depth knowledge of context. There is a need to enrich user profiles to achieve the personalized services with the ability to adapt the user’s context. The context can be reflected in a particular kind of knowledge and hence modeled as ontology. Ontology based context models are effective means to handle complex situations that support the sharing or integration of context information. This paper presents ontology based context model using OWL for adaptive mobile devices. It models the context over its four major elements including device, user, environment (location and time) and activity. The proposed ontology was derived in different classes, relationships, associations, dependencies and constraints to model dynamic context. Ontologies present a standardized, consistent and shareable context model. The context model and consequent context snapshots can be acknowledged by AUI to present a suitable user interface. The ontology was developed using Protégé on the basis of each context type having different values. Semantic querying (SPARQL) was used for knowledge acquisition. Moreover, the Pellet and HermiT Reasoner were used to verify the rules, relations and constraints to avoid the inconsistency between classes. Comparative to other context models for adaptive interfaces, ontological model provides more of scalability and growth with learning new context in to the shared context knowledge. INDEX TERMS Adaptive user interface, context aware interface, ontology driven interfaces, knowledge representation, knowledge engineering.

UX of AI & PersonalizationResearch Papers

On-demand Personalized Explanation for Transparent RecommendationMouadh Guesmi · 2021The literature on explainable recommendations is already rich. In this paper, we aim to shed light on an aspect that remains under- explored in this area of research, namely providing personalized explanations. To address this gap, we developed a transparent Rec- ommendation and Interest Modeling Application (RIMA) that pro- vides on-demand personalized explanations with varying levels of detail to meet the demands of different types of end-users. The results of a preliminary qualitative user study demonstrated po- tential benefits in terms of user satisfaction with the explainable recommender system. Our work would contribute to the litera- ture on explainable recommendation by exploring the potential of on-demand personalized explanations, and contribute to the prac- tice by offering suggestions for the design and appropriate use of personalized explanation interfaces in recommender systems. CCS CONCEPTS • Information systems →Personalization; Recommender sys- tems.

UX of AI & PersonalizationResearch Papers

Personalization in Practice: Methods and ApplicationsDmitri Goldenberg · 2021Personalization is one of the key applications in machine learning with widespread usage across e-commerce, entertainment, pro- duction, healthcare and many other industries. While various ma- chine learning techniques present novel state-of-the-art advances and super-human performance year-over-year, personalization and recommender-systems applications are often late-adopters of novel solutions due to problem hardness and implementation complexity. This tutorial presents recent advances across the personaliza- tion industry and demonstrates their practical applications in real case-studies of world-leading online platforms. Key trends such as deep learning, causality and active exploration with bandits are depicted with real examples and demonstrated alongside their busi- ness considerations and implementation challenges. Rising topics like explainability, fairness, natural interfaces and content gener- ation are covered, touching on aspects of both technology and user experience. Our tutorial relies on recent advances in the field and on work conducted at Booking.com, where we implement per- sonalization models on one of the world’s leading online travel platform. CCS CONCEPTS • Information systems →Personalization; Recommender sys- tems.

UX of AI & PersonalizationResearch Papers

PRIME: A Personalized Recommender System for Information Visualization Methods via Extended Matrix CompletionChen, Lau · 2021Adapting user interface designs for specific tasks performed by different users is a challenging yet important problem. Automatically adapting visualization designs to users and contexts (e.g., tasks, display devices, environments, etc.) can theoretically improve human–computer interaction to acquire insights from complex datasets. However, effectiveness of any specific visualization is moderated by individual differences in knowledge, skills, and abilities for different contexts. A modeling framework called Personalized Recommender System for Information visualization Methods via Extended matrix completion (PRIME) is proposed for recommending the optimal visualization designs for individual users in different contexts. PRIME quantitatively models covariates (e.g., psychological and behavioral measurements) to predict recommendation scores (e.g., perceived complexity, mental workload, etc.) for users to adapt the visualization specific to the context. An evaluation study was conducted and showed that PRIME can achieve satisfactory recommendation accuracy for adapting visualization, even when there are limited historical data. PRIME can make accurate recommendations even for new users or new tasks based on historical wearable sensor signals and recommendation scores. This capability contributes to designing a new generation of visualization systems that will adapt to users' states. PRIME can support researchers in reducing the sample size requirements to quantify individual differences, and practitioners in adapting visualizations according to user states and contexts.

UX of AI & PersonalizationResearch Papers

Recommendation Engines: Traditional vs Deep Learning ApproachesAniket Dhawa, Suranjana Sarkar et al. · 2021With digitization growing by leap and bounds, websites are now overloaded with products and information density, making it challenging for customers to choose between a variety of products. The Recommendation engine enables companies to deliver their customers more personalized and customized products or information efficiently. As a result, the recommendation engine eventually helps in boosting different business performance metrics such as revenues, Click-Through Rates (CTRs), conversions, etc., for these companies significantly. And in the long run, it also optimizes user experience, thus translating to higher customer satisfaction and retention. In this paper, we will highlight some of the contemporary algorithms used in building high-performance recommendation engines, illustrate use cases from Tech Giants that can be applied in the context of the banking and financial services domain and identify major challenges that are open for further research work. Also, in this paper, we will be using recommendation engine and recommender system alternatively as both are synonymous. Index Terms—Recommendation engine, recommender system, Machine Learning, Deep Learning, content based recommenda- tion, collaborative filtering, State of Art Technique (SOAT)

UX of AI & PersonalizationResearch Papers

SemanticAdapt: Optimization-based Adaptation of Mixed Reality Layouts Leveraging Virtual-Physical Semantic ConnectionsYifei Cheng∗ · 2021We present an optimization-based approach that automatically adapts Mixed Reality (MR) interfaces to different physical envi- ronments. Current MR layouts, including the position and scale of virtual interface elements, need to be manually adapted by users ∗This work was done while Yifei Cheng was an intern at Tsinghua University. †The corresponding author. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. UIST’21, October 2021, Virtual © 2021 Association for Computing Machinery. ACM ISBN 978-1-4503-8635-7/21/10...$15.00 https://doi.org/10.1145/3472749.3474750 whenever they move between environments, and whenever they switch tasks. This process is tedious and time consuming, and ar- guably needs to be automated for MR systems to be beneficial for end users. We contribute an approach that formulates this challenge as a combinatorial optimization problem and automatically decides the placement of virtual interface elements in new environments. To achieve this, we exploit the semantic association between the virtual interface elements and physical objects in an environment. Our optimization furthermore considers the utility of elements for users’ current task, layout factors, and spatio-temporal consistency to previous layouts. All those factors are combined in a single linear program, which is used to adapt the layout of MR interfaces in real time. We demonstrate a set of application scenarios, showcasing the versatility and applicability of our approach. Finally, we show that compared to a naive adaptive baseline approach that does not 282 UIST’21, October 2021, Virtual Cheng and Yan, et al. take semantic associations into account, our approach decreased the number of manual interface adaptations by 33%. CCS CONCEPTS • Human-centered computing →Mixed / augmented reality; Virtual reality; User interface management systems.

UX of AI & PersonalizationResearch Papers

Effect of Adaptive Guidance and Visualization Literacy on Gaze Attentive Behaviors and Sequential Patterns on Magazine-Style Narrative VisualizationsOSWALD BARRAL, SÉBASTIEN LALLÉ et al. · 2021We study the effectiveness of adaptive interventions at helping users process textual documents with embedded visualizations, a form of multimodal documents known as Magazine-Style Narrative Visualizations (MSNVs). The interventions are meant to dynamically highlight in the visualization the datapoints that are described in the textual sentence currently being read by the user, as captured by eye-tracking. These interventions were previously evaluated in two user studies that involved 98 participants reading excerpts of real-world MSNVs during a 1-hour session. Participants' outcomes included their subjective feedback about the guidance, and well as their reading time and score on a set of comprehension questions. Results showed that the interventions can increase comprehension of the MSNV excerpts for users with lower levels of a cognitive skill known as visualization literacy. In this article, we aim to further investigate this result by leveraging eye-tracking to analyze in depth how the participants processed the interventions depending on their levels of visualization literacy. We first analyzed summative gaze metrics that capture how users process and integrate the key components of the narrative visualizations. Second, we mined the salient patterns in the users' scanpaths to contextualize how users sequentially process these components. Results indicate that the interventions succeed in guiding attention to salient components of the narrative visualizations, especially by generating more transitions between key components of the visualization (i.e., datapoints, labels, and legend), as well as between the two modalities (text and visualization). We also show that the interventions help users with lower levels of visualization literacy to better map datapoints to the legend, which likely contributed to their improved comprehension of the documents. These findings shed light on how adaptive interventions help users with different levels of visualization literacy, informing the design of personalized narrative visualizations.

UX of AI & PersonalizationResearch Papers

Deep Learning for Emotion Driven User ExperiencesCarmen Bisogni, Lucia Cascone et al. · 2021Deep knowledge about user characteristics and behaviors opens new and promising landscapes to the User Experience. Thanks to emerging machine learning techniques, a new form of communication chan- nel among users and applications may be exploited for customizing and finely tuning the dynamic behav- ior of applications to the peculiarities of their users. This work investigates the empathic improvement of the User Experiences and exploits inferences on user expressions for activating gaming and entertain- ment events, that are adopted in a cinematic way to create dynamic application behaviors. The presented approach is applied to a third/first-person horror adventure and a classic table game. As already verified in a preliminary phase of the research, user impressions, collected as subjective evaluation in a controlled experiment, are positive and encourage further steps, like the evaluation of other inferences on users. © 2021 Published by Elsevier B.V.

UX of AI & PersonalizationResearch Papers

An Emotion-Oriented Problem Space for UIMina Alipour · 2021Emotions significantly affect the human interaction process with computers. They constitute a dynamic user’s charac- teristic that can be crucial for adapting user interfaces (UI) to the user’s context. However, their impact on UI adaptation concepts and techniques is not well understood. This paper proposes a problem space that studies the design dimensions to be considered in UI adaptation to emotions. In particular, this emotion-oriented problem space for UI adaptation, highlights the temporal aspects of emotions. It is obtained from two literature reviews realized to identify the gaps in existing problem spaces and the potentials of including emotional aspects inside them. The proposed problem space can benefit all adaptive systems that aim to improve users’ and system’s performance based on users’ reactions to the system. Index Terms—User interface adaptation, Problem space, Emo- tion adaptation,Temporal adaptation.

UX of AI & PersonalizationResearch Papers

UX designers pushing AI in the enterprise: a case for adaptive UIsJohn Zimmerman · 2020- → Adaptive user interfaces (AUIs) provide an untapped opportunity for UX designers to innovate with AI. - → AUIs seem well suited for enterprise applications, where it is easier to estimate the value of saving a worker time by automating mundane and repetitive tasks. - → AUIs raise new ethical questions about user privacy and about the unintended impacts of making workers faster at their work.

UX of AI & PersonalizationResearch Papers

Rise of Machine Agency: A Framework for Studying the Psychology of Human – AI Interaction (HAII)S. Shyam Sundar · 2020*Advances in personalization algorithms and other applications of machine learning have vastly enhanced the ease and convenience of our media and communication experiences, but they have also raised significant concerns about privacy, transparency of technologies and human control over their operations. Going forth, reconciling such tensions between machine agency and human agency will be important in the era of artificial intelligence (AI), as machines get more agentic and media experiences become increasingly determined by algorithms. Theory and research should be geared toward a deeper understanding of the human experience of algorithms in general and the psychology of Human–AI interaction (HAII) in particular. This article proposes some directions by applying the dual-process framework of the Theory of Interactive Media Effects (TIME) for studying the symbolic and enabling effects of the affordances of AI-driven media on user perceptions and experiences.*

UX of AI & PersonalizationResearch Papers

Towards User-Centric Intervention Adaptiveness: Influencing Behavior-Context Based Healthy Lifestyle InterventionsReceived September · 2020In the era of digital well-being, smart gadgets are the unobtrusive sources of acquiring infor- mation. A variety of personalized wellness applications support self-quantification based recommendations to provide wellness status for achieving personalized targets. However, these applications are unable to promote the induction of new healthy habits and thus are not too much effective for long term as users tend to loose their interest. Thus, we have proposed a methodology for User-Centric Adaptive Intervention based on behavior change theory for maintaining end-users’ interest. The methodology consists of four steps: (1) quantification of behavior based on contributing factors governed by expert-driven rules; (2) behavior-context based mapping for the identification of behavior status of the user; (3) selection of appropriate way of intervention to get fruitful outcomes; and finally (4) feedback based evaluation on the basis of recorded activities and questionnaires for satisfaction. A comprehensive healthy behavior index- based quantification supports the machine learning-based prediction model for behavior-context mapping. Furthermore, the evaluation is performed through implicit and explicit feedback analysis along with the accuracy of the behavior-context prediction model through multiple scenarios to cover comprehensive situations. The ensemble classifier suggests the accuracy of 98.02% for the behavior-context prediction model, which is higher than the other classifiers. The gain in behavior change is drawn from implicit feedback, which depicts that behavior context-based methods have improved the adaptation in behavior at a steady pace for the long term. The explicit feedback from 99 end-users of wellness application based on the proposed methodology obtained Good and Desired status for widely used System Usability Score and AttrakDiff tools respectively. INDEX TERMS User behavior, behavior-context, lifestyle, lifelog monitoring, self-quantification, healthy behavior index, adaptive interventions.

UX of AI & PersonalizationResearch Papers

An Effective Clustering‑Based Web Page Recommendation Framework for E‑Commerce WebsitesHarpreet Singh · 2020The burgeoning e-commerce market has presented companies with the opportunity to grow their businesses through online platforms. But, the researchers have concluded that just 2.86% of e-commerce website visits lead to a purchase and one of the reasons for this missed opportunity is an unpleasant website browsing experience. Therefore, a pleasant browsing experience is the need of the hour whereby the web page recommendation systems (WPRS) provide high-quality navigation experience by providing suggestions about the web pages of interest and by taking the website users to their desired web pages in fewer clicks. In this context, this paper presents a method to improve the browsing experience of the website users by propos- ing two hybrid algorithms based on clustering for web page recommendation systems, namely a hybrid partitioning-based heuristic sequence clustering (HSC) algorithm inspired from K-medoid and DBSCAN algorithms and a hybrid tree-based sequence clustering (TSC) algorithm inspired from B-Trees and BIRCH algorithm. The testing has been performed using CTI, BMSWebView1, BMSWebView2 and MSNBC datasets. To measure the performance, the algorithm considered for the study has been evaluated using parameters like precision, recall, F1 measures and execution time. Also, an in-depth comparative analysis of state-of-the-art web page recommendation systems with the recommendation system considered for the study has been done. The results indicate that the proposed clustering-based framework was able to generate superior results than the other classes of algorithms.

UX of AI & PersonalizationResearch Papers

Human-Machine Interaction Personalization: aMonica La Mura, Patrizia Lamberti · 2020The increasing spread of pervasive technology has led to the fast development of human-centered connected systems, such as cloud-based voice services, assisted driving systems, domotics control systems, personal digital assistants. The user interacts with these systems by speaking to an artificial intelligence, which interprets the speaker’s requests and takes decision accordingly. In such scenario, the real-time collection of personal information from the speaker’s voice is a key- function to develop in order to offer personalized services. Gender is part of the basic information needed to customize the user experience. Furthermore, knowledge about the sex of the speaker also proves useful in automatic speaker recognition and voice-based identity recognition systems, since it restricts the search space to individuals of one gender, thus speeding up the system response. Therefore, gender recognition techniques through speech analysis have largely attracted the researchers’ attention. Speech analysis is usually performed by extracting some features from the speech signal that can be affected by additional factors other than the gender: emotional state of the speaker, for example, is conveyed in the speech by altering some parameters that take part to the gender recognition process. At the same time, the outcome of emotion recognition systems based on speech analysis can be affected by the speaker’s gender. This paper briefly summarizes the techniques used to perform gender recognition through speech analysis and proposes a practice to take gender into account in emotion recognition methods.

UX of AI & PersonalizationResearch Papers

Artificial intelligence in recommender systemsZhang · 2020Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial intelligence (AI), particularly computational intelligence and machine learningmethodsandalgorithms,hasbeennaturallyappliedinthedevelopmentofrecommendersystemstoimproveprediction accuracy and solve data sparsity and cold start problems. This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of recommender systems. The paper not only reviews cutting-edge theoretical and practical contributions, but also identifies current research issues and indicates new research directions. It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning. The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI.

UX of AI & PersonalizationResearch Papers

A Long-Term Evaluation of Adaptive Interface Design for Mobile Transit InformationAlexander Haig · 2020Personalization of user experience has a long history of success in the HCI community. More recently the community has focused on adaptive user interfaces, supported by machine learning, that reduce interaction efforts and improves user experience by collaps- ing transactions and pre-filtering results. However, generally, these more recent results have only been demonstrated in the laboratory environment. In this paper, we share the case of a deployed mobile transit app that adapts based on users’ previous usage. We examine the impact of adaptation, both good and bad, and user abandon- ment rates. We conducted an 18-month assessment where 2,616 participants (with and without vision impairments) were recruited and participated in an A/B study. Finally, we draw some insights on some unusual effects that appear over the long term. CCS CONCEPTS • Human-centered computing →Empirical studies in inter- action design.

UX of AI & PersonalizationResearch Papers

Self-adaptation of Workflow Business Software to the User's Requirements and BehaviorUser Interface, User Experience et al. · 2020The main goal of the presented paper is to propose a method for adaptation of the user interface of workflow software to increase its efficiency, reduce the number of errors, and improve its UX. The authors assumed that the system adaptation will be achieved by application to intelligent methods for modeling user as well as system. In order to do this, a special tool for data gathering has been designed and in the next steps of the research, this tool will be also implemented in a real environment. A unique value of the paper is that after many years of theoretical research, the first attempt to implement a practical solution for self-adapting and the personalized interface for workflow systems was done. Janusz Sobecki et al. / Procedia Computer Science 176 (2020) 3506–3513 3507 adaptation [3]. This AI approach for user interface adaptation may be also enhanced with the application of user interface ontologies [4]. 1.1. Related Works In user interface design we should start with proper user model [1] however, we should always remember that ever-increasing number of users, especially of web-based systems, also brings the increase of differences among their users and interaction styles [3]. The user differences may reflex their demographical, psychological as well as sociological user characteristics, which have an influence on the users' information needs and interaction habits. The consequence of these differences is causing difficulties in modeling these users in the standard way [1], so since many years more sophisticated solutions have been proposed, for example, one enhanced with the application of ontologies [5], which was further applied in SOA systems development [4] and [6]. The information systems design and development have been enhanced by ontologies on at least several different levels, such as database integration, business logic, or Graphical User Interfaces (GUI) [6]. The before mentioned work presents an approach for mapping formal ontologies to GUI. This supports device-independent GUI construction and semi-automatic GUI modeling. This issue was also been raised in other work [7], as well as [8]. User interfaces have been also adopted by means of application different recommender methods such as Demographic Filtering, Content-Based Filtering, Collaborative Filtering or Hybrid Approach [9, 15], wherein user grouping or classification different machine learning algorithms may be applied, or the recommender method hybridization may be based

UX of AI & PersonalizationResearch Papers

Customer Experiences in the Age of Artificial IntelligenceNisreen Ameen, Ali Tarhini et al. · 2020Artificial intelligence (AI) is revolutionising the way customers interact with brands. There is a lack of empirical research into AI-enabled customer experiences. Hence, this study aims to analyse how the integration of AI in shopping can lead to an improved AI-enabled customer experience. We propose a theoretical model drawing on the trust-commitment theory and service quality model. An online survey was distributed to customers who have used an AI- enabled service offered by a beauty brand. A total of 434 responses were analysed using partial least squares-structural equation modelling. The findings indicate the significant role of trust and perceived sacrifice as factors mediating the effects of perceived convenience, personalisation and AI-enabled service quality. The findings also reveal the significant effect of relationship commitment on AI-enabled customer experience. This study contributes to the existing literature by revealing the mediating effects of trust and perceived sacrifice and the direct effect of relationship commitment on AI-enabled customer experience. In addition, the study has practical implications for retailers deploying AI in services offered to their customers.

UX of AI & PersonalizationResearch Papers

User interface design patterns and ontology models for adaptive mobile applications2020Mobile applications are an essential element in pervasive and ubiquitous computing, and they face many challenges during their generation process from the analysis of user needs to the design of specific mobile interfaces and their development in several technological platforms. Moreover, the rise of Ambient Intelligent and context-aware environments also introduces multiple interaction aspects to be considered when using mobile devices in this kind of scenarios. The present work seeks to examine the role of design patterns and ontology models in order to help with the generation of mobile applications, which can be adapted at runtime to the various user needs, different context scenarios, interactive design modes, or technology requirements. In this way, an ontology-based framework is introduced to represent, design, and support the adaptation of user interfaces in mobile appli- cations by using design patterns according to these user needs or preferences and the context around them. This framework provides developers with a client-server architecture that enables the access to an expert knowledge base of user, context, and pattern information together with a set of inference rules, which allow the dynamic selection of interface design patterns and the runtime adaptation of the user interface features. These ontology models and inference rules are key components of the proposed framework, and their implementation has helped to produce an example of mobile application supporting user interface adap- tation processes for disabled people, which can be required in Ambient Intelligent environments. Three examples of user scenarios have been considered to assess the framework potential, and usability dimensions have been tested by a limited set of users through the produced mobile application, making the usefulness of generated adaptive user interfaces apparent.

UX of AI & PersonalizationResearch Papers

Proactive vs. Reactive Personalization: Can Customization of Privacy Enhance User Experience?Bo Zhang · 2019Online recommender systems have triggered widespread privacy concerns due to their reliance on personal user data for providing personalized services. To address these concerns, some systems have started allowing users to express their preferences before receiving personalized content (i.e., reactive personalization) rather than automatically pushing it to them (i.e., proactive personalization). However, this would mean constant calls for user action, which can adversely affect user experience. One potential solution is to offer users the ability to customize their privacy settings at the outset, thus obviating the need for constant consultation. We conducted a 2 (Personalization: Reactive vs. Proactive) X 3 (Customization of Settings: Absence vs. Action vs. Cue) factorial experiment (N = 299) with a movie recommendation system. Findings show that interface cues suggesting customization enhance user experience, even in the presence of proactive personalization. They also highlight the important role played by negative privacy experiences in the past.

UX of AI & PersonalizationResearch Papers

Measuring the impact of online personalization - Past, present and futureMarkus Zanker⁎, Laurens Rookb et al. · 2019Research on understanding, developing and assessing personalisation systems is spread over multiple disciplines and builds on methodologies and findings from several different research fields and traditions, such as Artificial Intelligence (AI), Machine Learning (ML), Human–Computer Interaction (HCI), and User Modelling based on (applied) social and cognitive psychology. The fields of AI and ML primarily focus on the optimisation of per- sonalisation applications, and concentrate on creating ever more accurate algorithmic decision makers and prediction models. In the fields of HCI and Information Systems, scholars are primarily interested in the phe- nomena around the use and interaction with personalisation systems, while Cognitive Science (partly) delivers the theoretical underpinnings for the observed effects. The aim and contribution of this work is to put together the pieces about the impact of personalisation and recommendation systems from these different backgrounds in order to formulate a research agenda and provide a perspective on future developments.

UX of AI & PersonalizationResearch Papers

Exploring a Design Space of Graphical Adaptive Menus: Normal vs. Small ScreensJEAN VANDERDONCKT · 2019Graphical Adaptive Menus are Graphical User Interface menus whose predicted items of immediate use can be automatically rendered in a prediction window. Rendering this prediction window is a key question for adaptivity to enable the end-user to efficiently differentiate predicted items from normal ones and to consequently select appropriate items. Adaptivity for graphical menus has been investigated more for normal screens, such as desktops, than for small screens, such as smartphones, where real estate imposes severe rendering constraints. To address this question, this article defines and explores a design space where graphical adaptive menus are structured based on Bertin's eight visual variables (i.e., position, size, shape, value, color, orientation, texture, and motion) and their combination by comparing their rendering for small screens with respect to normal screens. Based on this design space, previously introduced graphical adaptive menus are revisited in terms of four stability properties (i.e., spatial, physical, format, and temporal), and new menu designs are introduced and discussed for both normal and small screens. The resulting set of graphical adaptive menu has been subject to a preference analysis from which a particular design emerged: the cloud menu, where predicted items are arranged in an adaptive tag cloud. We investigate empirically the effect of the cloud menu on the item selection time and the error rate with respect to a static menu and an adaptive linear menu. This article then suggests a set of usability guidelines for designers and practitioners to design graphical adaptive menus in general and cloud menus in particular.

UX of AI & PersonalizationResearch Papers

Context- and Data-driven Satisfaction Analysis of User Interface Adaptations Based on Instant User FeedbackUsability testing · 2019Modern User Interfaces (UIs) are increasingly expected to be plastic, in the sense that they retain a constant level of usability, even when subjected to context (platform, user, and environment) changes at runtime. Adaptive UIs have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. However, evaluating end-user satisfaction of adaptive UIs is a challenging task, because the UI and the context-of-use are both constantly changing. Thus, an acceptance analysis of UI adaptation features should consider the context-of-use when adaptations are triggered. Classical usability evaluation methods like usability tests mostly focus on a posteriori analysis techniques and do not fully exploit the potential of collecting implicit and explicit user feedback at runtime. To address this challenge, we present an on-the-fly usability testing solution that combines continuous context monitoring together with collection of instant user feedback to assess end-user satisfaction of UI adaptation features. The solution was applied to a mobile Android mail application, which served as basis for a usability study with 23 participants. A data-driven end-user satisfaction analysis based on the collected context information and user feedback was conducted. The main results show that most of the triggered UI adaptation features were positively rated.

UX of AI & PersonalizationResearch Papers

Self-Adaptation in Mobile Apps: a SystematicGrua · 2019With their increase, smartphones have become more integral components of our lives but due to their mobile nature it is not possible to develop a mobile application the same way another software system would be built. In order to always provide the full service, a mobile application needs to be able to detect and deal with changes of context it may be presented with. A suitable method to achieve this goal is self-adaptation. However, as of today it is difficult to have a clear view of existing research on self-adaptation in the context of mobile applications. In this paper, we apply the systematic literature review methodology on selected peer-reviewed papers focusing on self- adaptability in the context of mobile applications. Out of 607 potentially relevant studies, we select 44 primary studies via carefully-defined exclusion and inclusion criteria. We use known modelling dimensions for self-adaptive software systems as our classification framework, which we apply to all selected primary studies. From the synthesized data we obtained, we produce an overview of the state of the art. The results of this study give a solid foundation to plan for future research and practice on engineering self-adaptive mobile applications.

UX of AI & PersonalizationResearch Papers

A survey of cyber‑physical system implementations of real‑time personalized interventionsRobert Steele · 2019Advances in sensor technology and machine learning as well as the widespread use of smartphones are shifting the focus of healthcare. Emerging paradigms such as cyber-physical systems (CPSs) make possible the transition from reactive to preventive care. CPSs can be implemented to achieve effective mobile health solutions and to provide sophisticated new mechanisms to monitor an individual’s state in real-time via the use of sensors and mobile devices. Despite the significant potential impact of such systems, their implementation poses a range of complex technical challenges. This article surveys the state-of-the-art in implementations of CPSs for real-time personalized interventions. A general three layer architecture which can be used to consider current implementations is first presented along with a description of its main components. We also propose a three level taxonomy in accordance with the system capabilities. Then, the principal technical challenges, human-machine interaction challenges and future directions are discussed. Fifteen of the state-of-the-art implementations are qualitatively evaluated in terms of sensor capabilities, just-in-time reaction, interruptibility, and adherence, among other characteristics. By reviewing the state-of-the-art of the systems that have been built to-date, the focus of the review is to summarize current technical challenges and future opportunities for both future CPS implementers and behavioral scientists designing CPS for personalized interventions.

UX of AI & PersonalizationResearch Papers

Integrated model-driven development of self-adaptive user interfacesEnes Yigitbas · 2019Modern user interfaces (UIs) are increasingly expected to be plastic, in the sense that they retain a constant level of usability, even when subjected to context changes at runtime. Self-adaptive user interfaces (SAUIs) have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. The development of SAUIs is a challenging and complex task as additional aspects like context management and UI adaptation have to be covered. In classical model-driven UI development approaches, these aspects are not fully integrated and hence introduce additional complexity as they represent crosscutting concerns. In this paper, we present an integrated model-driven development approach where a classical model-driven development of UIs is coupled with a model-driven development of context-of-use and UI adaptation rules. We base our approach on the core UI modeling language IFML and introduce new modeling languages for context- of-use (ContextML) and UI adaptation rules (AdaptML). The generated UI code, based on the IFML model, is coupled with the context and adaptation services, generated from the ContextML and AdaptML model, respectively. The integration of the generated artifacts, namely UI code, context, and adaptation services in an overall rule-based execution environment, enables runtime UI adaptation. The benefit of our approach is demonstrated by two case studies, showing the development of SAUIs for different application scenarios and a usability study which has been conducted to analyze end-user satisfaction of SAUIs.

UX of AI & PersonalizationResearch Papers

I see, you design: user interface intelligent design system with eye tracking and interactive genetic algorithmCheng · 2019User interfaces are important for streamlining the interactions between humans and computers. However, there are few effective approaches for collecting users’ preferences implicitly and objectively for the purpose of user interface (UI) design optimization. This paper presents an effective approach to interactive genetic algorithm (IGA) optimization-based UI design via eye tracking, including eye-movement data based users’ preference inferring, gene coding for real UI components and design features, and the visualization and interaction mechanisms. Then we design and build a prototype system that applies IGA to generate and optimize music player UI solutions automatically. An evaluation of our prototype system suggests that it can generate and identify personalized UI designs reliably with minimal user intervention and high efficiency and user satisfaction.

UX of AI & PersonalizationResearch Papers

Personalized Explanations for Hybrid Recommender SystemsPigi Kouki · 2019Recommender systems have become pervasive on the web, shaping the way users see information and thus the decisions they make. As these systems get more complex, there is a growing need for transparency. In this paper, we study the problem of generating and visualizing personalized explanations for hybrid recommender sys- tems, which incorporate many different data sources. We build upon a hybrid probabilistic graphical model and develop an approach to generate real-time recommendations along with personalized explanations. To study the benefits of explanations for hybrid rec- ommender systems, we conduct a crowd-sourced user study where our system generates personalized recommendations and explana- tions for real users of the last.fm music platform. We experiment with 1) different explanation styles (e.g., user-based, item-based), 2) manipulating the number of explanation styles presented, and 3) manipulating the presentation format (e.g., textual vs. visual). We apply a mixed model statistical analysis to consider user personal- ity traits as a control variable and demonstrate the usefulness of our approach in creating personalized hybrid explanations with different style, number, and format. CCS CONCEPTS • Information systems →Decision support systems; Collabo- rative filtering; • Human-centered computing →Social network- ing sites; Empirical studies in visualization.

UX of AI & PersonalizationResearch Papers

Information Dashboards and Tailoring Capabilities - A Systematic Literature ReviewAndrea Vazques-Ingelmo · 2019The design and development of information dashboards are not trivial. Several factors must be accounted; from the data to be displayed to the audience that will use the dashboard. However, the increase in popularity of these tools has extended their use in several and very different contexts among very different user profiles. This popularization has increased the necessity of building tailored displays focused on specific requirements, goals, user roles, situations, domains, etc. Requirements are more sophisticated and varying; thus, dashboards need to match them to enhance knowledge generation and support more complex decision-making processes. This sophistication has led to the proposal of new approaches to address personal requirements and foster individualization regarding dashboards without involving high quantities of resources and long development processes. The goal of this work is to present a systematic review of the literature to analyze and classify the existing dashboard solutions that support tailoring capabilities and the methodologies used to achieve them. The methodology follows the guidelines proposed by Kitchenham and other authors in the field of software engineering. As results, 23 papers about tailored dashboards were retrieved. Three main approaches were identified regarding tailored solutions: customization, personalization, and adaptation. However, there is a wide variety of employed paradigms and features to develop tailored dashboards. The present systematic literature review analyzes challenges and issues regarding the existing solutions. It also identifies new research paths to enhance tailoring capabilities and thus, to improve user experience and insight delivery when it comes to visual analysis. INDEX TERMS SLR, systematic literature review, tailoring, custom, personalized, adaptive, information dashboards.

UX of AI & PersonalizationResearch Papers

A Framework for the Development of a Dynamic AdaptiveVivien Johnston · 2019 The aim of this paper is to present PhD research that aims to enhance the User Experience by proposing a framework that combines the three core components of: dynamic interfaces; adaptive interfaces; and intelligent interfaces. Initial research into the field has identified a gap at the intersection of these types of interaction. A dynamic interaction understands the user, their device and their physical environment to provide a basic User Experience. An adaptive interaction understands the user’s capabilities further to implement an enhanced experience via usability and accessibility whilst recognising the flow of the user and their pipeline. The intelligent interaction builds further upon this through the incorporation of Machine Learning algorithms that assist in making the interface intelligent and provide a personalised experience for each user based upon their end goal. This in turn will reduce a user’s cognitive load and enhance their interactive experience with an interface. CCS CONCEPTS • Human-centered computing~Human computer interaction (HCI) • Human-centered computing~Usability testing • Computing methodologies~Machine learning Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. ECCE 2019, September 10-13, 2019, BELFAST, United Kingdom © 2019 Association for Computing Machinery. ACM ISBN 978-1-4503-7166-7/19/09...$15.00 https://doi.org/10.1145/3335082.3335125

UX of AI & PersonalizationResearch Papers

SAM: A Modular Framework for Self-Adapting Web MenusCamille Gobert · 2019This paper presents SAM, a modular and extensible JavaScript framework for self-adapting menus on webpages. SAM al­ lows control of two elementary aspects for adapting web menus: (1) the target policy, which assigns scores to menu items for adaptation, and (2) the adaptation style, which spec­ ifes how they are adapted on display. By decoupling them, SAM enables the exploration of diferent combinations inde­ pendently. Several policies from literature are readily imple­ mented, and paired with adaptation styles such as reordering and highlighting. The process—including user data logging— is local, ofering privacy benefts and eliminating the need for server-side modifcations. Researchers can use SAM to experiment adaptation policies and styles, and benchmark techniques in an ecological setting with real webpages. Prac­ titioners can make websites self-adapting, and end-users can dynamically personalise typically static web menus. CCS CONCEPTS • Human-centered computing → Human computer in­ teraction (HCI); User interface design;

UX of AI & PersonalizationResearch Papers

Walking with Adaptive Augmented Reality Workspaces: Design and Usage PatternsCCS CONCEPTS · 2019Mobile augmented reality may eventually replace our smartphones as the primary way of accessing information on the go. However, current interfaces provide little support to walking and to the va- riety of actions we perform in the real world. To achieve its full potential, augmented reality interfaces must support the fluid way we move and interact in the physical world. We explored how dif- ferent adaptation strategies can contribute towards this goal. We evaluated design alternatives through contextual studies and identi- fied the key interaction patterns that interfaces for walking should support. We also identified desirable properties of adaptation-based interface techniques, which can be used to guide the design of the next-generation walking-centered augmented reality workspaces. CCS CONCEPTS • Human-centered computing →Mixed / augmented reality; Graphical user interfaces; Empirical studies in HCI.

UX of AI & PersonalizationResearch Papers

Customizing User Experience with Adaptive Virtual RealityBachar Senno · 2018Virtual reality is becoming more prominent than ever. The improved performance of mobile devices, along with the growing interest in more immersive user experiences, have allowed virtual reality to gain solid standing in this regard: nowadays, a smartphone and a pair of VR goggles are all that is needed for a full VR experience. The uses of VR systems extend beyond home entertainment: in particular, we are tackling the possibility that VR can be used by therapists to aid patients suffering from neuro- developmental disorders (NDD) [1,2]. To that, an adaptive system that can change different aspects of the VR environment, using machine learning techniques, can go a long way in increasing the efficiency of NDD treatments.

UX of AI & PersonalizationResearch Papers

Towards incorporating personality into the design of an interface: a method for facilitating users' interaction with the displaySamer Muthana Sarsam, Hosam Al-Samarraie · 2018A novel user interface (UI) design based on the personality characteristics of users was proposed and examined in a mobile learning context. It was argued that differences in personality can stimulate individuals’ information processing capabili- ties in according to their display preferences, thus an effective visual experience. The personality characteristics and design preferences of 87 students (37 male, and 50 female) were collected and analysed. The clustering result (using k-means algorithm) revealed two potential personality types, which we call the neuroticism and the extra- conscientiousness groups. Then, an interface was designed for each personality group using the association rules method. An eye-tracking device was used to record changes in participants’ eye-pupil diameter and fixation duration, and thus examine their cogni- tive load and attention. The participants’ eye movement data of each group showed that their visual experience was significantly improved when using the interface designed based on their personality characteristics. This work offers some important design and practical insights to the human–computer interaction and the design of mobile device UI.

UX of AI & PersonalizationResearch Papers

Rule based adaptive user interface for adaptive E-learning systemManohara Pai M. M. · 2018The term Adaptive E-learning System (AES) refers to the set of techniques and approaches that are combined together to offer online courses to the learners with the aim of providing customized resources and interfaces. Most of these systems focus on adaptive contents which are generated to the learners without considering the learning styles of the learners. Learning style of the learner defines the way of learning the contents. The system should not only meet the individual need of the contents but also the customized user interface on the portal. Hence, an AES should mainly focus on recommending learning contents with Adaptive User Interface (AUI) on the portal. The work in the paper proposes a generic approach to provide the learning contents with AUI components based on the learning styles of the learners. The learning style adopted for the work is the Felder-Silverman Learning Style Model (FSLSM). The proposed approach defines generic rules which are generated automatically for any online course with the adaptive contents. Also, the approach takes care of new learners by providing learning path as a user interface component on the portal. The experiment has been conducted on engineering students for a particular online course. The portal is validated using parameters of usability testing by generating test cases and statistical analysis has been carried out to identify the impact of AUI components on the learning process. The result shows the well adaptation of user interface components and contents based on learning styles.

UX of AI & PersonalizationResearch Papers

“This App Would Like to Use Your Current Location to Better Serve You”: Importance of User Assent and System Transparency in Personalized Mobile ServicesTsai-Wei Chen · 2018Modern mobile apps aim to provide personalized services without appearing intrusive. A common strategy is to let the user initiate the service request (e.g., “click here to receive coupons for your favorite products”), a practice known as “overt personalization.” Another strategy is to assuage users’ privacy concerns by being transparent about how their data would be collected, utilized and stored. To test these two strategies, we conducted a 2 (Personalization: Overt vs. Covert) x 2 (Transparency: High vs. Low) factorial experiment, with a fifth control condition. Participants (N=302) interacted with GreenByMe, a prototype of an eco-friendly mobile application. Data show that overt personalization affects perceived control. Significant three-way interactions between power usage, perceived overt personalization and perceived information transparency was seen on perceived ease of use, trust in the app, user engagement and behavioral intention to use the app in the future. In addition, results reveal that perceived information transparency also promotes trust, which is negatively linked with privacy concerns and positively correlated with user engagement and product involvement. Author Keywords Location-Aware Computing; Contextual Computing; Usability Study; User Experience Design; Privacy ACM Classification Keywords H.5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous.

UX of AI & PersonalizationResearch Papers

Deep Sequential Recommendation for Personalized Adaptive User InterfacesUnknown · 2017Adaptive user-interfaces (AUIs) can enhance the usability of complex software by providing real-time contextual adapta- tion and assistance. Ideally, AUIs should be personalized and versatile, i.e., able to adapt to each user who may perform a variety of complex tasks. But this is difficult to achieve with many interaction elements when data-per-user is sparse. In this paper, we propose an architecture for personalized AUIs that leverages upon developments in (1) deep learning, par- ticularly gated recurrent units, to efficiently learn user inter- action patterns, (2) collaborative filtering techniques that en- able sharing of data among users, and (3) fast approximate nearest-neighbor methods in Euclidean spaces for quick UI control and/or content recommendations. Specifically, inter- action histories are embedded in a learned space along with users and interaction elements; this allows the AUI to query and recommend likely next actions based on similar usage patterns across the user base. In a comparative evaluation on user-interface, web-browsing and e-learning datasets, the deep recurrent neural-network (DRNN) outperforms state-of- the-art tensor-factorization and metric embedding methods. Author Keywords Adaptive User Interface; Deep Learning; Personalization ACM Classification Keywords I.2.6. Artificial Intelligence: Learning; H.5.2 Information In- terfaces and Presentation(e.g. HCI): User Interfaces

UX of AI & PersonalizationResearch Papers

Designing Responsive Interactive Applications by Emotion-Tracking and Pattern-Based Dynamic User Interface AdaptationSanim Rashi, Christian Herdin · 2016Model-based user interface development environments (MB-UIDEs) can be enhanced by pattern-based frameworks to allow for richer design capa‐ bilities and more flexible responsive behavior during the runtime of the imple‐ mented target application. In this paper an experimental system prototype for integrating facial analysis and eye-tracking into a pattern-based dynamic user interface adaptation process is discussed. The resulting system evaluates the emotional state of the system users to trigger the HCI-pattern-based adaptation of the user interface. By monitoring the emotional states of the users over longer time periods, while the system changes its behavior and its appearance, conclu‐ sions about the perceived quality dimensions of the interactive application can be drawn.

UX of AI & PersonalizationResearch Papers

Model-based adaptive user interface based on context and user experience evaluationJamil Hussain, Anees Ul Hassan, Hafiz Syed Muhammad Bilal, Rahman Ali, Muhammad Afzal, Shujaat Hussain, Jaehun Bang,Oresti Banos, Sungyoung Lee · 2016Personalized services have greater impact on user experience to effect the level of user satisfaction. Many approaches provide personalized services in the form of an adaptive user interface. The focus of these approaches is lim- ited to specific domains rather than a generalized approach applicable to every domain. In this paper, we proposed a domain and device-independent model-based adaptive user interfacing methodology. Unlike state-of-the-art approaches, the proposed methodology is dependent on the evaluation of user context and user experience (UX). The proposed methodologyisimplementedasanadaptiveUI/UXauthoring (A-UI/UX-A) tool; a system capable of adapting user inter- face based on the utilization of contextual factors, such as user disabilities, environmental factors (e.g. light level, noise level, and location) and device use, at runtime using the adap- tation rules devised for rendering the adapted interface. To validate effectiveness of the proposed A-UI/UX-A tool and methodology, user-centric and statistical evaluation methods are used. The results show that the proposed methodology B Sungyoung Lee [sylee@oslab.khu.ac.kr](mailto:sylee@oslab.khu.ac.kr) Jamil Hussain [jamil@oslab.khu.ac.kr](mailto:jamil@oslab.khu.ac.kr) 1 Department of Computer Science & Engineering, Kyung Hee University (Global Campus), 1732 Deokyoungdae-ro, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Republic of Korea 2 Quaid-e-Azam College of Commerce, University of Peshawar, Peshawar, Pakistan 3 College of Electronics and Information Engineering, Sejong University, Seoul, South Korea 4 Telemedicine Group, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Postbox 217, 7500 AE Enschede, The Netherlands outperforms the existing approaches in adapting user inter- faces by utilizing the users context and experience.

UX of AI & PersonalizationResearch Papers

Smartphone Accelerometer Data used for Detecting Human EmotionsAndreas Færøvig Olsen, Jim Torresen · 2016The paper outlines work on the classification of emotions using smartphone accelerometer data. Such classi- fication can be used, in conjunction with other methods of emotion detection, to adapt services to the user’s emotional state. The data is collected from individuals who have been carrying their phone in a pocket while walking. An Android app was developed in order to monitor the smartphone accelerometer of the individuals who participated in the study and occasionally requested them to judge and submit their emotional state. This way, data is collected from a natural environment rather than a laboratory setting. The recorded data is then processed and used to train different classifiers to be compared. The machine learning algorithms decision tree, support vector machine and multilayer perceptron are used for this purpose. Emotions are classified in two dimensions: pleasantness and arousal (activation). While the recognition rate for the arousal dimension is promising at 75%, pleasantness is harder to predict, with a recognition rate of 51%. These findings indicate that by only analyzing accelerometer data recorded from a smartphone, it is possible to make predictions of a person’s state of activation.

UX of AI & PersonalizationResearch Papers

Adaptive user modelling in car racing games using behavioural and physiological dataTheodosis Georgiou, Yiannis Demiris · 2015Personalised content adaptation has great potential to increase user engage- ment in video games. Procedural generation of user-tailored content increases the self-motivation of players as they immerse themselves in the virtual world. An adap- tive user model is needed to capture the skills of the player and enable automatic game content altering algorithms to fit the individual user. We propose an adaptive user modelling approach using a combination of unobtrusive physiological data to iden- tify strengths and weaknesses in user performance in car racing games. Our system creates user-tailored tracks to improve driving habits and user experience, and to keep engagement at high levels. The user modelling approach adopts concepts from the Trace Theory framework; it uses machine learning to extract features from the user’s physiological data and game-related actions, and cluster them into low level primi- tives. These primitives are transformed and evaluated into higher level abstractions such as experience, exploration and attention. These abstractions are subsequently used to provide track alteration decisions for the player. Collection of data and feed- back from 52 users allowed us to associate key model variables and outcomes to user responses, and to verify that the model provides statistically significant decisions per- sonalised to the individual player. Tailored game content variations between users in our experiments, as well as the correlations with user satisfaction demonstrate that our algorithm is able to automatically incorporate user feedback in subsequent procedural content generation. B Theodosis Georgiou theodosis.georgiou08@imperial.ac.uk Yiannis Demiris y.demiris@imperial.ac.uk 1 Personal Robotics Laboratory, Department of Electrical and Electronic Engineering, Imperial College London, Exhibition Road, South Kensington, London SW7 2BT, UK 123 268 T. Georgiou, Y. Demiris

UX of AI & PersonalizationResearch Papers

Towards a Human-centered E-Commerce Personalization FrameworkMarios Belk · 2015This paper presents a personalization framework, namely PersonaWeb that adapts the visual and interaction design of E-Commerce Web environments based on human cognitive differences. In particular, it describes a user model formalization that incorporates a set of human cognitive factors (i.e., cognitive styles and working memory capacity) and an adaptation engine that personalizes the visual and interaction design attributes of E-Commerce product views. The proposed framework has been applied in a real-life E-Commerce Web-site and two subsequent user studies were conducted in which 135 users interacted with the personalized and the original (non-personalized) version of the same Web environment. Results indicate the added value of personalizing content and functionality of E-Commerce product views in terms of users’ task completion performance.

UX of AI & PersonalizationResearch Papers

e-Learning Experience using Recommender SystemsJesus Bobadill, Antonio Hernando et al. · 2011This paper presents the results obtained using a real e-learning recommender system where the collaborative filtering core has been adapted with the aim of weighting the importance of the recommendations in accordance with the users’ knowledge. In this way, ratings from users with better knowledge of the given subject will have greater importance over ratings from users with less knowledge. In the same way, we validate the results obtained and we adjust, with just one parameter, the weight that should be awarded, in each specific e-learning recommender system, to the ratings of the users with the best reputation. The results obtained show a notable improvement regarding traditional collaborative filtering methods and suggest balanced weightings between the importance assigned to users with more or less knowledge. Categories and Subject Descriptors K.3.2 [Computer and Information Science Education] General Terms Experimentation.

UX of AI & PersonalizationResearch Papers

Persuasive strategies and emotional states: towards designing personalized and emotion-adaptive persuasive systems2023Persuasive strategies have been widely operationalized in systems or applications to motivate behaviour change across diverse domains. However, no empirical evidence exists on whether or not persuasive strategies lead to certain emotions to inform which strategies are most appropriate for delivering interventions that not only motivate users to perform target behaviour but also help to regulate their current emotional states. We conducted a large-scale study of 660 participants to investigate if and how individ- uals including those at different stages of change respond emotionally to persuasive strategiesandwhy.Specifically,weexaminedtherelationshipbetweenperceivedeffec- tiveness of individual strategies operationalized in a system and perceived emotional states for participants at different stages of behaviour change. Our findings estab- lished relations between perceived effectiveness of strategies and emotions elicited in individuals at distinct stages of change and that the perceived emotions vary across stages of change for different reasons. For example, the reward strategy is associated with positive emotion only (i.e. happiness) for individuals across distinct stages of change because it induces feelings of personal accomplishment, provides incentives that increase the urge to achieve more goals, and offers gamified experience. Other strategies are associated with mixed emotions. Our work links emotion theory with behaviour change theories and stages of change theory to develop practical guidelines for designing personalized and emotion-adaptive persuasive systems. B Oladapo Oyebode oladapo.oyebode@dal.ca Darren Steeves darren@jackhabbit.com Rita Orji rita.orji@dal.ca 1 Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 1W5, Canada 2 School of Health and Human Performance, Dalhousie University, Halifax, NS B3H 4R2, Canada 123 1176 O. Oyebode et al.

Emotional DesignResearch Papers

A Novel User Emotional Interaction Design Model Using Long and Short-Term Memory Networks and Deep Learning2021Emotional design is an important development trend of interaction design. Emotional design in products plays a key role in enhancing user experience and inducing user emotional resonance. In recent years, based on the user's emotional experience, the design concept of strengthening product emotional design has become a new direction for most designers to improve their design thinking. In the emotional interaction design, the machine needs to capture the user's key information in real time, recognize the user's emotional state, and use a variety of clues to finally determine the appropriate user model. Based on this background, this research uses a deep learning mechanism for more accurate and effective emotion recognition, thereby optimizing the design of the interactive system and improving the user experience. First of all, this research discusses how to use user characteristics such as speech, facial expression, video, heartbeat, etc., to make machines more accurately recognize human emotions. Through the analysis of various characteristics, the speech is selected as the experimental material. Second, a speech-based emotion recognition method is proposed. The mel-Frequency cepstral coefficient (MFCC) of the speech signal is used as the input of the improved long and short-term memory network (ILSTM). To ensure the integrity of the information and the accuracy of the output at the next moment, ILSTM makes peephole connections in the forget gate and input gate of LSTM, and adds the unit state as input data to the threshold layer. The emotional features obtained by ILSTM are input into the attention layer, and the self-attention mechanism is used to calculate the weight of each frame of speech signal. The speech features with higher weights are used to distinguish different emotions and complete the emotion recognition of the speech signal. Experiments on the EMO-DB and CASIA datasets verify the effectiveness of the model for emotion recognition. Finally, the feasibility of emotional interaction system design is discussed.

Emotional DesignResearch Papers

Emotion recognition and its applications2014The paper proposes a set of research scenarios to be applied in four domains: software engineering, website customization, education and gaming. The goal of applying the scenarios is to assess the possibility of using emotion recognition methods in these areas. It also points out the problems of defining sets of emotions to be recognized in different applications, representing the defined emotional states, gathering the data and training. Some of the scenarios consider possible reactions of an affect-aware system and its impact on users.

Emotional DesignResearch Papers

Personas and user-centered design: How can personas benefit product design processes?Tomasz Miaskiewicz, Kenneth A. Kozar · 2011This paper investigates personas, an alternative method for representing and communicating customer needs. By using a narrative, picture, and name, a persona provides product designers with a vivid representation of the design target. Numerous benefits of incorporating personas into product design approaches have been suggested, but the present literature fails to identify the most significant and universal advantages of persona use. By incorporating expert opinion through the use of Delphi methodology, this research first examines the benefits of incorporating personas into design processes. After gaining consensus on the perceived importance of the individual benefits, this paper then elaborates on the most significant benefits of persona use and needed future research on the personas method.

Emotional DesignResearch Papers

Towards customized emotional design: An explorative study of user personality and user interface skin preferences2005Two key elements in the area of cognitive ergonomics are user-system performance and the user similarity. However, with the introduction of skinnable user interfaces, a technology that gives the user interface a chameleon-like ability, elements such as aesthetic, fun, and especially user individuality and identity become more important. This paper presents two explorative studies on user personality in relation to skin preferences. In the studies participants were asked to rate their preference of a set of Windows Media Player skins and to complete the BIS/BAS and the IPIP-NEO personality inventories. The results of the first study suggest colour and similarity-attraction as two possible underlying factors for the correlations found between personality traits and skin preferences. The results of the second study partly confirm these findings, however not for similar personality traits and skin types correlations.

Emotional DesignResearch Papers

Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI LiteratureShuning Zhang, Hui Wang et al. · 2025As Artificial Intelligence (AI) increasingly becomes an active collaborator in co-creation, understanding the distribution and dynamic of agency is paramount. The Human-Computer Interaction (HCI) perspective is crucial for this analysis, as it uniquely reveals the interaction dynamics and specific control mechanisms that dictate how agency manifests in practice. Despite this importance, a systematic synthesis mapping agency configurations and control mechanisms within the HCI/CSCW literature is lacking. Addressing this gap, we reviewed 134 papers from top-tier HCI/CSCW venues (e.g., CHI, UIST, CSCW) over the past 20 years. This review yields four primary contributions: (1) an integrated theoretical framework structuring agency patterns, control mechanisms, and interaction contexts, (2) a comprehensive operational catalog of control mechanisms detailing how agency is implemented; (3) an actionable cross-context map linking agency configurations to diverse co-creative practices; and (4) grounded implications and guidance for future CSCW research and the design of co-creative systems, addressing aspects like trust and ethics.

HCI & Adaptive InterfacesResearch Papers

Behavioural Intervention Technology in UX Design: Conceptual Review, Synthesis, and Research DirectionYoungsoo Shin, Chajoong Kim et al. · 2021The notion of Behavioural Intervention Technologies (BITs) has received increasing attention from researchers and practitioners creating design and technology to support users' behavioural change processes. However, the value of consolidating previous findings to connect insightful research and practical design works and generating new knowledge on BITs has been under-recognized in the design community. To support researchers and practitioners who struggle with creating humancentred BITs to effectively induce changes in human behaviour, this paper investigates existing literature on BITs through a systematic literature review with 28 selected publications. From this literature review process, this paper proposes a structured synthesis of extant perspectives and approaches, offering an overarching perspective for effective applications of the BIT design approach. At the end of the paper, these findings are extended to discuss further research and implementations of design practices for changes in human behaviours.

HCI & Adaptive InterfacesResearch Papers

Computer Science ReviewMaaruf Ali, Peter S. Excell · 2021A review of research on universal usability, plasticity of user interface design and facilitation of interface development with universal usability is presented. The survey was based on 165 research papers spanning over fifty-five years. The foundations of adaptive or intelligent user interfaces (AUI or IUI) are presented, three core domains being focused upon: Artificial Intelligence (AI), User Modelling (UM) and Human–Computer Interaction (HCI). For comparison of the various AUIs, a proposed taxonomy is given. One conclusion is that an efficient training vector for fast optimal convergence of the machine-learning algorithm is a necessity, but key to this is the bounding of the dataset, the goal being to achieve an accurate user preference model, which has to be built from a limited number of datasets obtained from the human interaction. More research also needs to be conducted to ascertain the usefulness and effectiveness of IUIs compared against AUIs. With the global mobility of users, interface design must take account of the abilities and cultures of users, derived from actual user behaviour and not on their feedback. A key question is whether the interface should be adaptive under system control or be made adaptable under user control. A need is identified for an ‘‘afferential component’’ that stores a priori information about the end user, an ‘‘inferential component’’ that determines to what extent the user interface actually needs to be adapted, and the ‘‘efferential component’’ that actually determines how the adaptivity is applied seamlessly to the system. Application to e-learning is a priority: the use of machine intelligence to achieve appropriate learnability, ideally enhanced by ‘‘Playful interaction’’, was found to be desirable. Universal application of adaptation lies in the future, but AUI properties cannot be ascertained while disregarding the other parameters of the system in which it will be used. A more complete understanding of the human mental model is necessary, requiring a highly multidisciplinary approach and cooperation between diverse researchers. Finally, a performance evaluation of plasticity of user interface was conducted: it is concluded that the use of dynamic techniques can enhance the user experience to a much greater extent than more basic approaches, although optimisation of usability parameter trade-offs needs further attention. It is noted that most of the work reviewed originated from a limited range of cultural pe

HCI & Adaptive InterfacesResearch Papers

Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to DesignQian Yang · 2020Artificial Intelligence (AI) plays an increasingly important role in improving HCI and user experience. Yet many chal­ lenges persist in designing and innovating valuable human-AI interactions. For example, AI systems can make unpredictable errors, and these errors damage UX and even lead to unde­ sired societal impact. However, HCI routinely grapples with complex technologies and mitigates their unintended conse­ quences. What makes AI different? What makes human-AI interaction appear particularly difficult to design? This paper investigates these questions. We synthesize prior research, our own design and research experience, and our observations when teaching human-AI interaction. We identify two sources of AI’s distinctive design challenges: 1) uncertainty surround­ ing AI’s capabilities, 2) AI’s output complexity, spanning from simple to adaptive complex. We identify four levels of AI sys­ tems. On each level, designers encounter a different subset of the design challenges. We demonstrate how these findings reveal new insights for designers, researchers, and design tool makers in productively addressing the challenges of human-AI interaction going forward. Author Keywords User experience, artificial intelligence, sketching, prototyping. CCS Concepts •Human-centered computing → Human computer inter­ action (HCI); Interaction design process and methods;

HCI & Adaptive InterfacesResearch PapersUX of AI & Personalization

Interactive architectural approach (interactive architecture): An effective and adaptive process for architectural designMojtaba Parsaee, Parinaz Motealleh et al. · 2015This research attempts to offer a new approach for architectural design process that the concepts of interaction and multi-relations are being achieved through it. This approach, which is identified as an interactive architecture, suggests a process that a dynamic and mutual relation will create among all factors and parameters of design and the effects of each factor or parameters on final design will be considered. In fact, the main axis of the suggested approach is comprehensive interaction with all various aspects of design, since the design problems have multi-aspects, mostly. Thus, the final design will be resulted based on these mutual relations and it is a production which will have a maximum and optimum adaption with all factors and parameters. What makes this atti- tude more significant and crucial is the chaotic situation that is produced in architecture and urban designing of most cities especially in developing countries and leads to lack of identity in these cities. So, the interactive approach can be able to eliminate the challenges and create the fields of sustain- able architecture and urban development through an effective method. However, this process encounters to some constraints and challenges along with some potentials which are discussed in this essay. The research method is analytical-interpretative and based on qualitative analyses. ª 2015 The Authors. Production and hosting by Elsevier B.V. on behalf of Housing and Building National Research Center. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

HCI & Adaptive InterfacesResearch Papers