The 2nd Workshop on Human-Centered Recommender Systems

• URL: http://arxiv.org/abs/2511.19979v1
• Date: Tue, 25 Nov 2025 06:49:14 GMT
• Title: The 2nd Workshop on Human-Centered Recommender Systems
• Authors: Kaike Zhang, Jiakai Tang, Du Su, Shuchang Liu, Julian McAuley, Lina Yao, Qi Cao, Yue Feng, Fei Sun,
• Abstract summary: Human-Centered Recommender Systems workshop calls for a paradigm shift from optimizing engagement toward designing systems that truly understand, involve, and benefit people.<n>Brings together researchers in recommender systems, human-computer interaction, AI safety, and social computing to explore how human values can be integrated into recommendation processes.<n>Centered around three thematic axes-Human Understanding, Human Involvement, and Human Impact-HCRS features keynotes, panels, and papers covering topics from LLM-based interactive recommenders to societal welfare optimization.
• Score: 37.23326108953788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender systems shape how people discover information, form opinions, and connect with society. Yet, as their influence grows, traditional metrics, e.g., accuracy, clicks, and engagement, no longer capture what truly matters to humans. The workshop on Human-Centered Recommender Systems (HCRS) calls for a paradigm shift from optimizing engagement toward designing systems that truly understand, involve, and benefit people. It brings together researchers in recommender systems, human-computer interaction, AI safety, and social computing to explore how human values, e.g., trust, safety, fairness, transparency, and well-being, can be integrated into recommendation processes. Centered around three thematic axes-Human Understanding, Human Involvement, and Human Impact-HCRS features keynotes, panels, and papers covering topics from LLM-based interactive recommenders to societal welfare optimization. By fostering interdisciplinary collaboration, HCRS aims to shape the next decade of responsible and human-aligned recommendation research.

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