Recommender systems profoundly shape how people discover information, make decisions, and interact with the digital world. As their societal influence grows, traditional metrics such as clicks, accuracy, or engagement no longer fully capture what truly matters to humans. The 2nd Workshop on Human-Centered Recommender Systems (HCRS) calls for a paradigm shift—from engagement-focused optimization toward systems that genuinely understand, empower, and benefit people. This workshop brings together researchers from recommender systems, HCI, AI safety, social computing, and responsible AI to explore the next generation of human-centered and socially aligned recommender technologies.
Important Dates
- Workshop paper submission: December 18, 2025
- Workshop paper notification: January 13, 2026
- Workshop paper camera-ready: February 2, 2026
- Workshops: TBD (Half-day session at TheWebConf 2026)
TIMEZONE: Anywhere On Earth (UTC-12)
Workshop Objectives
This workshop encourages researchers to propose new theoretical frameworks, interdisciplinary approaches, and perspectives for human-centered recommender systems. We aim to:
- Foster discussions on the evolving field of Human-Centered Recommender Systems.
- Encourage interdisciplinary approaches that bridge recommender systems, HCI, AI safety, and social computing.
- Share innovative methodologies, evaluation frameworks, and applications that advance human-aligned and socially responsible recommenders.
- Advocate for the adoption of advanced technologies, such as large language models, to enhance the human-centric qualities of recommender systems.
- Promote diversity and inclusion within the recommender systems community.
Call for Papers
HCRS is organized around three main thematic axes that call for a paradigm shift—from optimizing engagement to creating systems that genuinely understand, empower, and respect humans:
Human Understanding—Systems that understand humans
Modeling user intents, cognition, and affect beyond surface interactions, moving beyond clicks toward multidimensional measures such as satisfaction and trust.
- Intent- and context-aware recommendation
- Cognitive and affective modeling
- LLM-based user modeling
- Human behavior uncertainty
- Beyond-click metrics: satisfaction, trust, emotion
Human Involvement—Humans in the loop
Enabling interactive, co-adaptive, and controllable systems that empower users to guide model updates, express preferences, and co-create content.
- Interactive and conversational recommendation
- Multi-turn and mixed-initiative interaction
- User feedback elicitation and controllable personalization
- Human–AI co-creation in content-generation scenarios
- User simulation for scalable and safe training and evaluation
Human Impact—Systems that affect humans and society
Addressing societal and ethical dimensions, including fairness, privacy, robustness, and transparency, and mitigating issues such as echo chambers and filter bubbles.
- Fairness and bias mitigation
- Diversity enhancement and echo chamber alleviation
- Privacy-preserving recommendation
- Robustness and security
- Transparency and accountability
- Evaluation, auditing, and governance frameworks
- Societal-welfare and well-being-oriented optimization
Emerging Cross-Domain Topics
- LLM-powered recommender and human preference alignment
- Multi-objective optimization for social good, balancing privacy, diversity, and satisfaction
- Human-centered evaluation methodologies and user studies
- Recommender systems for education, healthcare, and other critical domains
Submission Guidelines
All submitted papers must be formatted as a single PDF document according to the ACM WWW 2026 template.
- Length: 4-8 pages (unlimited pages for references)
- Review Policy: Single-blind review
- Evaluation Criteria: Relevance to the workshop, scientific novelty, and technical quality
Submission Portal: The 2nd Workshop on Human-Centered Recommender Systems @TheWebConf 2026
Program Highlights
This workshop will be conducted over a half-day session featuring:
- Keynote Talks: Three 30-minute keynote talks delivered by leading researchers in HCRS, providing valuable insights into the latest advancements and future prospects.
- Paper Sessions: Presentations of accepted papers showcasing cutting-edge research.
- Panel Discussion: Senior researchers will discuss future directions and challenges in human-centered recommender systems.
Organizing Committee
The workshop is organized by an international and diverse team of experts in the field:
- Kaike Zhang (University of Chinese Academy of Sciences, China)
- Jiakai Tang (Renmin University of China, China)
- Du Su (Institute of Computing Technology, Chinese Academy of Sciences, China)
- Shuchang Liu (Kuaishou Technology, China)
- Julian McAuley (University of California, San Diego, USA)
- Lina Yao (CSIRO Data61, Australia)
- Qi Cao (Institute of Computing Technology, Chinese Academy of Sciences, China)
- Yue Feng (University of Birmingham, UK)
- Fei Sun (University of Chinese Academy of Sciences, China)
- Shixuan Zhang (University of Chinese Academy of Sciences, China)