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User in RSs 2024 : Call for Papers – Special Issue of the International Journal of Human-Computer Studies on Re-centering the User in Recommender System Research

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Link: https://sisinflab.github.io/ijhcs-user-in-rs/
 
When N/A
Where N/A
Abstract Registration Due Jul 15, 2024
Submission Deadline Oct 30, 2024
Final Version Due Feb 15, 2025
 

Call For Papers

International Journal of Human-Computer Studies (IJHCS), Elsevier

CiteScore: 11.9
Impact Factor: 5.4


Recommender systems (RSs), driven by advanced technology and evolving user demands, intricately analyze data to tailor content, products, or services, offering personalized experiences and enhancing user engagement. Cutting-edge technologies are redefining the nature of the interaction between users and RSs and amplifying the classic challenges of user engagement, highlighting the necessity of building, maintaining, evaluating, and studying systems that are fair, transparent, ethical, and primarily beneficial to users and society, upholding user expectations and fundamental human rights. Consequently, the ever-evolving landscape of RSs urgently demands a shift in focus: from prioritizing technology and stakeholder interests to empowering users and placing them at the core of research.

This special issue fosters collaboration among researchers from computer science, human-computer interaction, psychology, and ethics. This issue will serve as a platform for groundbreaking research that not only advances the technical frontiers of RSs but also ensures these systems are designed with a focus on user welfare, ethical standards, and societal impact. Contributions may range from innovative user-centric evaluation strategies prioritizing user trust and fairness to dynamic recommendation processes integrating direct user feedback and conversational interaction for enhanced personalization, e.g., with LLMs. Explorations into the explainability of RSs, personalization techniques that respect individual decision-making styles, and the ethical implications of bias, fairness, and privacy in data-driven systems are particularly welcome.

Topics of interest include but are not limited to:

User control and user-centric approaches
Trustworthiness and privacy
Explanation and transparency
Bias and fairness
Psychological aspects of recommendation
Conversational and user-driven RSs
User studies and user-centric evaluation
User interfaces and novel UI design studies

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