The ACM Recommender Systems conference (RecSys) is the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems. Recommendation is a particular form of information filtering, that exploits past behaviors and user similarities to generate a list of information items that is personally tailored to an end-user’s preferences. As RecSys brings together the main international research groups working on recommender systems, along with many of the world’s leading e-commerce companies, it has become the most important annual conference for the presentation and discussion of recommender systems research. RecSys 2020, the fourteenth conference in this series, will be held in Rio de Janeiro, Brazil. It will bring together researchers and practitioners from academia and industry to present their latest results and identify new trends and challenges in providing recommendation components in a range of innovative application contexts. In addition to the main technical track, RecSys 2020 program will feature keynote and invited talks, tutorials covering state-of-the-art in this domain, a workshop program, an industrial track and a doctoral symposium.
Published papers will go through a rigorous full peer review process. The conference proceedings, which will be available via the ACM Digital Library, are expected to be widely read and cited.
ACM RecSys 2020 will take place in Rio de Janeiro, Brazil, from September 22-26, 2020.
Topics of interest for RecSys include but are not limited to (alphabetically ordered):
Algorithm scalability, performance, and implementations
Bias, bubbles and ethics of recommender systems
Case studies of real-world implementations
Context-aware recommender systems
Conversational recommender systems
Economic models and consequences of recommender systems
Evaluation metrics and studies
Explanations and evidence
Interfaces for recommender systems
Novel machine learning approaches to recommendation algorithms (deep learning, reinforcement learning, etc.)
Privacy and Security
Voice, VR, and other novel interaction paradigms