Report on the 1st Workshop on Human-Centered Recommender Systems (HCRS 2025) at TheWebConf 2025

This report summarizes the 1st Workshop on Human-Centered Recommender Systems (HCRS), held on April 29, 2025, in conjunction with TheWebConf 2025. Recommender systems have become quintessential applications of human-computer interaction, offering significant convenience in daily life while simultaneously presenting challenges such as the information cocoon effect, privacy concerns, and fairness issues. Consequently, this workshop was established to provide a platform for researchers to explore the development of recommender systems that prioritize human needs, values, and capabilities at the core of their design and operation. Topics covered included robustness, privacy, transparency, fairness, diversity, accountability, ethical considerations, and user-friendly design. Through these discussions, the workshop sought to foster a collaborative environment for sharing insights on implementing these properties and exploring innovative evaluation metrics, ultimately advancing the field toward more ethical, user-centric, and socially responsible recommender systems. Date: 29 April 2025. Website: https://human-centeredrec.github.io/.

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Publication Details

Journal
ACM SIGIR Forum
Published
2025-12-01
DOI
https://doi.org/10.1145/3799914.3799921
Primary Topic
Recommender Systems and Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Report on the 1st Workshop on Human-Centered Recommender Systems (HCRS 2025) at TheWebConf 2025

Kaike Zhang, Yougang Lyu, Du Su, Yunfan Wu et al.
ACM SIGIR Forum
Recommender Systems and Techniques
article

Report on the 1st Workshop on Human-Centered Recommender Systems (HCRS 2025) at TheWebConf 2025

Kaike Zhang, Yougang Lyu, Du Su, Yunfan Wu, Yingqiang Ge, Qi Cao, Fei Sun, Zhaochun Ren, Shuchang Liu
article en

Abstract

This report summarizes the 1st Workshop on Human-Centered Recommender Systems (HCRS), held on April 29, 2025, in conjunction with TheWebConf 2025. Recommender systems have become quintessential applications of human-computer interaction, offering significant convenience in daily life while simultaneously presenting challenges such as the information cocoon effect, privacy concerns, and fairness issues. Consequently, this workshop was established to provide a platform for researchers to explore the development of recommender systems that prioritize human needs, values, and capabilities at the core of their design and operation. Topics covered included robustness, privacy, transparency, fairness, diversity, accountability, ethical considerations, and user-friendly design. Through these discussions, the workshop sought to foster a collaborative environment for sharing insights on implementing these properties and exploring innovative evaluation metrics, ultimately advancing the field toward more ethical, user-centric, and socially responsible recommender systems. Date: 29 April 2025. Website: https://human-centeredrec.github.io/.

ACM SIGIR ForumVol. 59(2)
Leiden University (NL), Amazon (United States) (US), State Administration of Work Safety (CN), University of Chinese Academy of Sciences (CN), University of Amsterdam (NL)
Openalex Percentile: Top 13%
Recommender Systems and Techniques
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