The 1st Workshop on Human-Centered Recommender Systems
Kaike Zhang, Yunfan Wu, Yougang lyu, Du Su, Yingqiang Ge, Shuchang, Liu, Qi Cao, Zhaochun Ren, Fei Sun

TL;DR
This workshop focuses on advancing human-centered recommender systems by emphasizing ethical, transparent, and user-focused design, addressing challenges like privacy, fairness, and user satisfaction through collaborative research and innovative evaluation methods.
Contribution
It introduces a platform for researchers to explore and discuss the development of ethical, transparent, and user-centric recommender systems with diverse evaluation approaches.
Findings
Discussion on integrating human needs and values into recommender systems
Identification of key challenges like privacy and fairness
Proposal of new metrics for user satisfaction and trust
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…
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Taxonomy
TopicsRecommender Systems and Techniques
