The 2nd Workshop on Human-Centered Recommender Systems

2025-11-25

AI Summary

Conventional recommender systems overemphasize engagement metrics—such as click-through rate—while neglecting foundational human-centered values including trustworthiness, fairness, transparency, and human well-being. Method: This workshop introduces “human-centered recommendation” as a novel paradigm, formalized through a three-dimensional framework: *understanding users*, *engaging users*, and *influencing users*. It advances value-aligned recommendation—shifting focus from behavioral optimization to ethical and societal value alignment—via large language model–driven interactive recommendation, social-welfare–oriented optimization algorithms, enhanced interpretability techniques, and robust fairness assurance mechanisms. Contribution/Results: The initiative fosters interdisciplinary integration across AI safety, human-computer interaction, and social computing. It establishes a responsible recommendation research consortium spanning theoretical foundations and real-world deployment, delivering a systematic roadmap for human-centered AI development over the next decade.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Application Domains: Humanities & Computational Social Science
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Recommender systems need to shift from engagement metrics to human benefit
• Integrating human values like trust and fairness into recommendation processes
• Addressing societal impact through interdisciplinary collaboration on human-centered design

Innovation

• Shifting from engagement to human-centered design
• Integrating human values into recommendation processes
• Fostering interdisciplinary collaboration for responsible research