We are pleased to invite you to contribute to the 18th ACM Conference on Recommender Systems (RecSys 2024), the premier venue for research on the foundations and applications of recommendation technologies. The upcoming RecSys conference will be held from October 14-18, 2024 in Bari, Italy. While there will be the option to attend remotely, authors of accepted papers are expected to present the work in person. The conference will continue RecSys’ practice of connecting researchers, practitioners, and students to exchange ideas, frame problems, and share solutions across a range of specialties concerned with recommendation. All accepted papers will be published by ACM.

We invite submissions of original research on all aspects of recommender systems, including contributions to algorithms ranging from collaborative filtering to knowledge-based reasoning or deep learning, contributions to design ranging from studies of human preferences and decision-making to novel interaction design, contributions to systems including practical issues of scale and deployment, contributions through applications that bring forward the lessons of innovative applications across various domains from e-commerce to education to social connections, and contributions through scientific inquiry on fundamental dynamics and impact of recommender systems. We welcome new research on recommendation technologies coming from diverse communities ranging from psychology to mathematics. In particular, we care as much about the human and economic impact of these systems as we care about their underlying algorithms. We encourage research papers coming from industry that focus on open challenges in their specific environment.

Topics of interest for RecSys 2024 include but are not limited to (alphabetically ordered):

    Algorithm scalability, performance, and implementations;
    Bias, fairness, bubbles, and ethics of recommender systems;
    Case studies of real-world implementations;
    Conversational and natural language recommender systems;
    Cross-domain recommendation;
    Data characteristics and processing challenges underlying recommender systems;
    Economic models and consequences of recommender systems;
    Evaluation methodology for recommender systems;
    Explanation interfaces for recommender systems;
    Large-language models as part of recommender systems;
    Multi-stakeholder recommendations;
    Novel approaches to recommendation, including voice, VR/AR, etc.;
    Preference elicitation;
    Privacy and security;
    Socially- and context-aware recommender systems;
    Systems challenges such as scalability, data quality, and performance;
    User studies of recommendation applications.

Papers on demonstration for RecSys should be submitted to the demo track, while papers on new resources for RecSys should be submitted to the reproducibility track. They would be desk-rejected in the main track.

We also point authors to the industry track for discussion of field experiences, deployments, user studies (etc.) that do not follow the framework of regular papers, or align with the reviewing guidelines below. A separate track is also included for late-breaking results papers; this track is intended for short presentations of preliminary work, mainly focused on fostering discussions with other members of the RecSys community.