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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

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

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

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

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

Influence of Artificial Intelligence Recommendation on Consumers' Purchase Intention Under the Information Cocoon EffectSiyi Liang, Nurzat Alimu et al. · 2023With the rapid development of e-commerce, artificial intelligence (AI) recommendation technology is also evolving rapidly. In the long run, the informa- tion obtained by consumers has become homogeneous, leading to the formation of “information cocoons effect”. However, few researches have examined the influ- ence of information cocoons effect on consumers’ purchase intention. Drawing on the Expectation Confirmation Model (ECM) and Information Ecology Theory, we develop a research model to investigate how information factor (Information Homogeneity), human factor (Personal Innovation), environment factor (Social Influence), and technology factor (Personalized Recommendation Quality, Plat- form Interaction Quality) affect consumers’ satisfaction and purchase intention. Data has been collected from 283 respondents and analyzed with SmartPLS 3.3.9. Findings indicate that Personalized Recommendation Quality, Platform Interac- tionQualitysatisfactionandPerceivedTimeRisksignificantlyinfluencesPurchase Intention. Potential theoretical and practical contributions are also discussed.

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

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

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

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

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

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

Interactive Music Genre Exploration with Visualization and Mood ControlYu Liang · 2021Recommender systems can be used to help users discover novel items and explore new tastes, for example in music genre explo- ration. However, little work has studied how to improve users’ understandability and acceptance of the novel items as well as sup- port users to explore a new domain. In this paper, we investigate how two different visualizations and mood control influence the perceived control, informativeness and understandability of a mu- sic genre exploration tool, and further to improve the helpfulness for new music genre exploration. Specifically, we compare a bar chart visualization used by earlier work to a contour plot which allows users to compare their musical preferences with both the recommended tracks as well as the new genre. Mood control is implemented with two sliders to set a preferred mood on energy and valence features (that correlate with psychological mood di- mensions). In the online user study, mood control was manipulated between subjects, and the visualizations were compared within subjects. During the study (N=102), we measured users’ subjective perceptions, experiences and the interactions with the system. Our results show that the contour plot visualization is perceived more helpful to explore new genres than the bar chart visualization, as the contour plot is perceived to be more informative and under- standable. Users spent significantly more time and used the mood control more in the contour plot than in the bar chart visualiza- tion. Overall, our results show that the contour plot visualization combined with mood control serves as the most helpful way for new music genre exploration, because the mood control is easier to understand and use when made transparent via an informative visualization. CCS CONCEPTS • Human-centered computing →User studies; Information visualization; User interface design; • Information systems → Recommender systems; Personalization. IUI ’21, April 14–17, 2021, College Station, TX, USA © 2021 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-8017-1/21/04. https://doi.org/10.1145/3397481.3450700

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

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

A tale of two recommender systems: The moderating role of consumer expertise on artificial intelligence based product recommendationsPipat Thontirawong b, Punjaporn Chinchanachokchai c · 2021Recommender systems are used in e-Commerce websites to make product recommendations or deliver person­ alized content to users. We constructed a beer recommendation program using review data from existing online community to test the hypotheses. This research aims to bridge the gap between marketing and computer science by investigating the moderating effects of consumer knowledge (expertise) on the performance and evaluation of two widely-used recommendation systems – user-based collaborative filtering and content-based. The results show that expert consumers prefer user-based collaborative filtering systems, whereas there is no difference between the two systems among novice consumers. Theoretical and managerial implications are discussed.

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

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

Exploring Mental Models for Transparent and Controllable Recommender Systems: A Qualitative StudyThao Ngo · 2020While online content is personalized to an increasing degree, e.g. us- ing recommender systems (RS), the rationale behind personalization and how users can adjust it typically remains opaque. This was often observed to have negative effects on the user experience and perceived quality of RS. As a result, research increasingly has taken user-centric aspects such as transparency and control of a RS into account, when assessing its quality. However, we argue that too little of this research has investigated the users’ perception and understanding of RS in their entirety. In this paper, we explore the users’ mental models of RS. More specifically, we followed the qualitative grounded theory methodology and conducted 10 semi- structured face-to-face interviews with typical and regular Netflix users. During interviews participants expressed high levels of un- certainty and confusion about the RS in Netflix. Consequently, we found a broad range of different mental models. Nevertheless, we also identified a general structure underlying all of these models, consisting of four steps: data acquisition, inference of user profile, comparison of user profiles or items, and generation of recommen- dations. Based on our findings, we discuss implications to design more transparent, controllable, and user friendly RS in the future. CCS CONCEPTS • Information systems →Recommender systems; • Human- centered computing →User studies.

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

To Explain or not to Explain: the Effects of Personal Characteristics when Explaining Music RecommendationsMartijn Millecamp · 2019Recommender systems have been increasingly used in online services that we consume daily, such as Facebook, Netflix, YouTube, and Spotify. However, these systems are often pre- sented to users as a “black box”, i.e. the rational for providing individual recommendations remains unexplained to users. In recent years, various attempts have been made to address this black box issue by providing textual explanations or interac- tive visualisations that enable users to explore the provenance of recommendations, and benefits in terms of precision and user satisfaction, among others, have been demonstrated. Pre- vious research had also indicated that personal characteristics such as domain knowledge, trust propensity and persistence may also play an important role on such perceived benefits. Yet, to date, little is known about the effects of personal char- acteristics when explaining recommendations. To address this gap, we developed a music recommender system with expla- nations and conducted an online study using a within-subject design. We captured various personal characteristics of par- ticipants and administered both qualitative and quantitative evaluation methods. Results indicated that personal character- istics have some significant influence on the interaction and perception of recommender systems and that this influence changes by adding explanations. Especially people with a low need for cognition benefited from explained recommendations. For people with a high need for cognition, we observed that explanations could lower their confidence. Based on these re- sults, we present some first design implications for explaining recommendations. 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. IUI ’19, © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. ISBN 123-4567-24-567/08/06...$15.00 DOI: http://dx.doi.org/10.475/123_4 ACM Classification Keywords H.5.2 Information Interfaces and Presentation (e.g. HCI): User-cen

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Moodplay: Interactive Music Recommendation Based on Artists' Mood SimilarityIvana Andjelkovic, Denis Parra et al. · 2018A large amount of research in recommender systems focuses on algorithmic accuracy and optimization of ranking metrics. However, recent work has unveiled the importance of other aspects of the recommendation process, including explanation, transparency, control and user experience in general. Building on these aspects, this paper introduces MoodPlay , an interactive music-artists recommender system which integrates content and mood-based filtering in a novel interface. We show how MoodPlay allows the user to explore a music collection by musical mood dimensions, building upon GEMS, a music-specific model of affect, rather than the traditional Circumplex model. We describe system architecture, algorithms, interface and interactions followed by use-case and offline evaluations of the system, providing evidence of the benefits of our model based on similarities between the typical moods found in an artist’s music, for contextual music recommendation. Finally, we present results of a user study (N = 279) in which four versions of the interface are evaluated with varying degrees of visualization and interaction. Results show that our proposed visualization of items and mood information improves user acceptance and understanding of both the underlying data and the recommendations. Furthermore, our analysis reveals the role of mood in music recommendation, considering both artists’ mood and users’ self-reported mood in the user study. Our results and discussion highlight the impact of visual and interactive features in music recommendation, as well as associated human-cognitive limitations. This research also aims to inform the design of future interactive recommendation systems.

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A systematic review and taxonomy of explanations in decision support and recommender systemsIngrid Nunes · 2017With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today’s increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice- giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated. B Ingrid Nunes ingridnunes@inf.ufrgs.br Dietmar Jannach dietmar.jannach@tu-dortmund.de 1 Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil 2 TU Dortmund, Dortmund, Germany 123 394 I. Nunes, D. Jannach

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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

Deep Neural Networks for YouTube RecommendationsPaul Covington, Jay Adams et al. · 2016YouTube represents one of the largest scale and most sophis- ticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and fo- cus on the dramatic performance improvements brought by deep learning. The paper is split according to the classic two-stage information retrieval dichotomy: first, we detail a deep candidate generation model and then describe a sepa- rate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintain- ing a massive recommendation system with enormous user- facing impact.

UX of AI & PersonalizationResearch Papers

Crowd-Based Personalized Natural Language Explanations for RecommendationsShuo Chang · 2016Explanations are important for users to make decisions on whether to take recommendations. However, algorithm gen- erated explanations can be overly simplistic and unconvinc- ing. We believe that humans can overcome these limita- tions. Inspired by how people explain word-of-mouth rec- ommendations, we designed a process, combining crowd- sourcing and computation, that generates personalized nat- ural language explanations. We modeled key topical as- pects of movies, asked crowdworkers to write explanations based on quotes from online movie reviews, and personal- ized the explanations presented to users based on their rat- ing history. We evaluated the explanations by surveying 220 MovieLens users, finding that compared to personalized tag- based explanations, natural language explanations: 1) con- tain a more appropriate amount of information, 2) earn more trust from users, and 3) make users more satisfied. This paper contributes to the research literature by describing a scalable process for generating high quality and personalized natural language explanations, improving on state-of-the-art content-based explanations, and showing the feasibility and advantages of approaches that combine human wisdom with algorithmic processes.

UX of AI & PersonalizationResearch Papers

Machine Learning Techniques for Recommender Systems – A Comparative Case AnalysisBinu Thomas · 2011Recommender System (RS) is one of the most popular applications of Artificial Intelligence which attracted researchers all around the world. Many machine learning algorithms are used to develop RSs. Choosing the best machine learning algorithm to provide users with a product or service is the most challenging task in the area of RSs. Now we are witnessing a paradigm shift in the purchase habits of people from in-shop to online resulting in the availability of online information exponentially growing every day. The ever-increasing online information and the number of online users create new avenues in RS. In an online shopping scenario, these systems must be able to recommend relevant items to the users. The RSs have to deal with the huge amount of information by filtering the relevant information based on the analysis made on the inputs made by the users during their online sessions. These systems can recommend appropriate items to users based on their interest and previous preference which can lead to increased sales. The three major techniques used to build a RS are content-based, collaborative based and hybrid-based. This paper presents the various applications of RSs and makes a detailed comparative study of different machine learning approaches used. The methodologies used for identifying research articles for analysis, the merits and demerits of different techniques in RSs and domain-specific applications of these techniques are well explained here with scientific review analysis.

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