We are pleased to invite you to contribute to the 17th ACM Conference on Recommender Systems (RecSys 2023), the premier venue for research on the foundations and applications of recommendation technologies. The upcoming RecSys conference will be held on September 18–22, 2023 in Singapore, with an inclusive format that accommodates remote attendance. Each accepted paper is expected to be presented in person. The conference will continue RecSys’ tradition 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; design ranging from studies of human preferences and decision-making to novel interaction design; systems including practical issues of scale and deployment; applications that bring forward the lessons of innovative applications across various domains from e-commerce to education to social connections; 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 2023 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
Interfaces for recommender systems
Multi-stakeholder recommendations
New aspects of recommender systems evaluation
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

Authors will be asked to assign a selection of predefined custom tags to describe their paper in the submission system. Tags can be assigned to indicate algorithms, interfaces, automated or user-centric evaluations, for example. Reviewers will also report their expertise across these tags, and this information will be used in review assignments.

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 provided 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.