The 2nd Workshop on Human-Centered Recommender Systems

TL;DR

This paper discusses the shift in recommender systems research towards human-centered approaches that prioritize human values like trust, safety, and fairness over traditional metrics such as accuracy and engagement.

Contribution

It introduces the Human-Centered Recommender Systems workshop, emphasizing interdisciplinary collaboration to integrate human values into recommendation algorithms.

Findings

Focus on human understanding, involvement, and impact in recommender systems

Highlighting the importance of trust, safety, and fairness in recommendations

Exploring LLM-based interactive recommenders and societal welfare optimization

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.