ACM Transactions on Recommender Systems (TORS) publishes high quality papers that address various aspects of recommender systems research, from algorithms to 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.