The 2nd Workshop on Human-Centered Recommender Systems

TL;DR

The paper advocates a paradigm shift to Human-Centered Recommender Systems (HCRS) within the WWW community, detailing three thematic axes—Human Understanding, Human Involvement, and Human Impact—that guide interdisciplinary collaboration across recommender systems, HCI, AI safety, and social computing. It argues that past metrics like accuracy and engagement are insufficient for human well-being, and promotes LLM-based interaction, fairness, privacy, transparency, and governance as core concerns. The workshop outlines its rationale, prior experience, objectives, target audience, and a diverse organizing team while proposing a half-day program with keynotes, papers, and panels to accelerate the adoption of human-aligned, societally beneficial recommendations. The practical impact is a structured, cross-disciplinary platform to develop evaluation frameworks, methodologies, and technologies that prioritize trust, safety, and well-being in real-world recommendation scenarios.

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.