One Size Does Not Fit All: Adaptive Interest Representation Learning for Multi-interest Recommendation

Sequential recommendation (SR) aims to predict users’ next interactions by modeling contextual patterns in their historical behavior sequences. Recent advancements in SR introduce multi-interest models that use multiple vectors to represent users’ diverse interests. However, existing methods predefine the number of interest vectors as a hyperparameter, limiting their adaptability to real-world scenarios where the number of interests varies across users. Our experiments show that this fixed-number interest representation suffers from a critical limitation: overfitting for users with simple interests, underfitting for users with complex interests, and a lack of interest prioritization. To address these limitations, we propose

daptive nterest epresentation learning for multi-interest ommendation (AirRec), a two-stage framework that adaptively adjusts the number of interest representations for each user’s behavior sequence. Specifically, the first stage focuses on interest foundation construction: to compensate for the lack of auxiliary information, we decompose item co-occurrence data to extract atomic interests, which act as the basic units for interest representation modeling. In the second stage, we conduct user-adaptive interest modeling, retaining only atomic interests relevant to the user’s interaction history to filter out noise and infer the actual number of underlying interests. The selected atomic interests are then aggregated into personalized, adaptively sized interest representations for each user sequence. The entire process is designed for parallel computation, guaranteeing efficiency in both training and inference. Experiments on three benchmark datasets demonstrate that AirRec improves NDCG@20 by an average of 14.94% and NDCG@50 by 24.73% compared to state-of-the-art methods. The source code is available at https://github.com/yaokunliu/AirRec.git.