ACM Transactions on Recommender Systems (TORS) publishes high quality papers that address various aspects of recommender systems research, from algorithms to the user experience, to questions of the impact and value of such systems, on a quarterly basis. The journal takes a holistic view on the field and calls for contributions from different subfields of computer science and information systems, such as machine learning, data mining, information retrieval, web-based systems, data science and big data, and human-computer interaction. Moreover, interdisciplinary research works are welcome as well. Such works may either be based on insights from related fields, e.g., marketing or psychology, or apply recommendation technology in novel application areas.
Announcements
ACM Updates Its Peer Review Policy
ACM is pleased to announce that its Publications Board has approved an updated Peer Review Policy. If you have any questions regarding the update, the associated FAQ addresses topics such as confidentiality, the use of large language models in the peer review process, conflicts of interest, and several other relevant concerns. If there are any issues that are not addressed in the FAQ, please contact ACM’s Director of Publications, Scott Delman.
New ACM Policy on Authorship
ACM has a new Policy on Authorship, covering a range of key topics, including the use of generative AI tools. Please familiarize yourself with the new policy and the associated list of Frequently Asked Questions.
