The 1st Workshop on Human-Centered Recommender Systems

Recommender systems are quintessential applications of human-computer interaction. Widely utilized in daily life, they offer significant convenience but also present numerous challenges, such as the information cocoon effect, privacy concerns, fairness issues, and more. Consequently, this workshop aims to provide a platform for researchers to explore the development of Human-Centered Recommender Systems (HCRS). HCRS refers to the creation of recommender systems that prioritize human needs, values, and capabilities at the core of their design and operation. In this workshop, topics will include, but are not limited to, robustness, privacy, transparency, fairness, diversity, accountability, ethical considerations, and user-friendly design. We hope to engage in discussions on how to implement and enhance these properties in recommender systems. Additionally, participants will explore diverse evaluation methods, including innovative metrics that capture user satisfaction and trust. This workshop seeks to foster a collaborative environment for researchers to share insights and advance the field toward more ethical, user-centric, and socially responsible recommender systems.

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Publication Details

Published
2025-05-08
DOI
https://doi.org/10.1145/3701716.3717736
Citations
1
Primary Topic
Recommender Systems and Techniques
Type
article
Field-Weighted Citation Impact
3.25
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The 1st Workshop on Human-Centered Recommender Systems

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1 citations
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article

The 1st Workshop on Human-Centered Recommender Systems

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

Abstract

Recommender systems are quintessential applications of human-computer interaction. Widely utilized in daily life, they offer significant convenience but also present numerous challenges, such as the information cocoon effect, privacy concerns, fairness issues, and more. Consequently, this workshop aims to provide a platform for researchers to explore the development of Human-Centered Recommender Systems (HCRS). HCRS refers to the creation of recommender systems that prioritize human needs, values, and capabilities at the core of their design and operation. In this workshop, topics will include, but are not limited to, robustness, privacy, transparency, fairness, diversity, accountability, ethical considerations, and user-friendly design. We hope to engage in discussions on how to implement and enhance these properties in recommender systems. Additionally, participants will explore diverse evaluation methods, including innovative metrics that capture user satisfaction and trust. This workshop seeks to foster a collaborative environment for researchers to share insights and advance the field toward more ethical, user-centric, and socially responsible recommender systems.

Leiden University (NL), Amazon (United States) (US), Chinese Academy of Sciences (CN), Institute of Computing Technology (CN), Amsterdam University of the Arts (NL), University of Chinese Academy of Sciences (CN), University of Amsterdam (NL)
Openalex Percentile: Top 8%
Recommender Systems and Techniques
3.25
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