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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

Machine Learning Algorithms for Improved Product Design User ExperienceXueli Wang, Bo Hu · 2024With the rapid advancement of technology and the increasing demand for user-centric products, the integration of machine learning techniques has become imperative. This paper explores the transformative potential of integrating Particle Swarm Optimization (PSO), Deep Reinforcement Learning (DRL), and other machine learning algorithms such as neural networks, decision trees, and support vector machines into product design processes. Our novel hybrid framework leverages PSO’s global search capabilities and DRL’s adaptive learning to optimize product designs in a manner that traditional methods cannot achieve. By employing predictive modeling, clustering, and recommendation systems, designers can gain valuable insights into user needs and preferences, facilitating the creation of more intuitive and personalized products. We demonstrate that this integrated approach significantly improves design efficiency and user satisfaction. Key findings include a 25% reduction in design iteration time and a 30% increase in user satisfaction scores compared to traditional optimization methods. Additionally, our methodology provides a flexible and scalable solution adaptable to various product design contexts, showcasing its broad applicability and effectiveness. The incorporation of real-time feedback mechanisms allows for continuous refinement and adaptation of product designs to meet evolving user expectations. This study contributes to the field by presenting a comprehensive, multi-technique optimization framework that bridges existing gaps and sets a new standard for user-centric product design optimization. Ultimately, this research underscores the significance of embracing machine learning as a powerful tool for revolutionizing the product design landscape and delivering superior user experiences. INDEX TERMS Machine learning algorithms, product design, user experience enhancement, user data analysis, predictive modeling.

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

Interface Design Optimization as a Multi-Armed Bandit ProblemJ. Derek Lomas, Jodi Forlizzi et al. · 2016“Multi-armed bandits” offer a new paradigm for the AI- assisted design of user interfaces. To help designers understand the potential, we present the results of two experimental comparisons between bandit algorithms and random assignment. Our studies are intended to show designers how bandits algorithms are able to rapidly explore an experimental design space and automatically select the optimal design configuration. Our present focus is on the optimization of a game design space. The results of our experiments show that bandits can make data-driven design more efficient and accessible to interface designers, but that human participation is essential to ensure that AI systems optimize for the right metric. Based on our results, we introduce several design lessons that help keep human design judgment in the loop. We also consider the future of human-technology teamwork in AI-assisted design and scientific inquiry. Finally, as bandits deploy fewer low- performing conditions than typical experiments, we discuss ethical implications for bandits in large-scale experiments in education. Author Keywords Educational games; optimization; data-driven design; multi- armed bandits; continuous improvement; ACM Classification Keywords H.5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous.

UX of AI & PersonalizationResearch Papers