Multi-Interest Recommendation: A Survey

Existing recommendation methods struggle to model users’ multifaceted preferences due to the diversity and volatility of user behavior, as well as the inherent uncertainty and ambiguity of item themes in practical scenarios. Multi-interest recommendation addresses this challenge by explicitly extracting multiple interest representations from users’ historical interactions, enabling fine-grained preference modeling and more accurate recommendations. It has attracted considerable attention in recommendation research. However, current recommendation surveys have either delved into specific recommendation tasks and downstream applications or focused on approaches that model users and items as single representations with cutting-edge techniques, overlooking users’ diverse preferences and the multifaceted aspects of items. In this work, we systematically review the progress, solutions, challenges, and future directions of multi-interest recommendation by answering the following three questions: (1) Why is multi-interest modeling significantly important for recommendation? (2) What aspects are focused on by multi-interest modeling in recommendation? and (3) How can multi-interest modeling be applied, along with the technical details of the representative modules? We hope that this survey establishes a fundamental framework and delivers a preliminary overview for researchers interested in this field and committed to further exploration. The implementation of multi-interest recommendation summarized in this survey is maintained at https://github.com/WHUIR/Multi-Interest-Recommendation-A-Survey .