The 20th ACM Conference on Recommender Systems (RecSys 2026), the leading conference for research on the foundations and applications of recommendation technologies, will take place from September 28 to October 2nd, 2026, in Minneapolis, Minnesota, USA.

We look forward to receiving your contributions for RecSys 2026. Below, you will find the descriptions of different tracks accepting contributions. The “In-Brief” and “Important Dates” sections of the CFP discuss key points of attention for this call. In the rest of the CFP, we provide detailed information that authors should thoroughly review when preparing their submissions.

Main track (handled by the program chairs):

    Long papers: We welcome high-impact original papers that contribute to all aspects of recommender systems. The paper length should be commensurate with the depth of contribution, comprehensiveness of analyses, and thorough discussion of related work. Each accepted paper will be included in the conference proceedings and presented at the conference. We expect the review process to be highly selective.
    Short papers: This track is intended for contributions that can be described completely and rigorously within a smaller page limit. These papers should present focused, self-contained research stories supported by experimental validations.
    Past, present and future papers: To mark the twentieth year of the RecSys Conference, this track encourages papers that consider a broad perspective on how the field has evolved and the challenges and directions that lay ahead. 

Relevant Areas & Topics

    Foundations of recommender systems
    Human-centered and interactive recommendation
    Explainability, transparency, and user control in recommender systems
    Fairness, safety, diversity, bias-mitigation, and societal impact of recommender systems
    Legal and ethical aspects of recommender systems
    Sustainable and eco-aware recommender systems
    Recommenders for multi-stakeholder, cross-domain, and multimodal contexts
    Generative, agentic, and reasoning-based recommendation
    Conversational, knowledge-based, and context-aware recommenders
    Recommendation evaluation methodologies and metrics beyond accuracy
    Recommender systems data, reproducibility, and benchmarking resources
    Real-world applications, case studies, and deployment insights of recommenders

Other tracks (handled by their respective chairs):

    Research and Practice Notes (replacing late-breaking results): short presentations of preliminary work, mainly focused on fostering discussions with other members of the RecSys community.
    Demo: implementations of novel, interesting, and important recommender systems’ concepts or applications.
    Reproducibility: contributions that discuss several aspects of reproducibility of empirical results, such as new resources or novel evaluation methodologies.
    Industry: papers that discuss field experiences, deployments, user studies and real-world challenges faced by industry practitioners.