Contrastive Flow Matching for Sequential Recommendation

Abstract

Flow Matching (FM), as a highly promising generative paradigm, has recently been introduced into sequential recommendation. However, the lack of explicit inter-user supervision in existing FM-based method may lead to an averaged flow phenomenon, where the model tends to produce similar velocity directions for different users during item generation. This greatly undermines the model’s ability to generate personalized item. To address this issue, we propose a Contrastive Flow Matching (ConFM) for sequential reconmendation, aiming to inject inter-user separation signals so as to preserve the discriminability of users’ velocity directions. Specifically, considering the common design where velocities are derived from predicted item representations, we introduce a Representation Contrastive Regularizer in the representation space, which forces the predicted item embeddings of different users to be separated from each other. In addition, we introduce a Velocity Contrastive Regularizer in the induced velocity space, which directly encourages each user’s induced velocity to stay far from other users’ velocity targets, thereby better aligning the generation process with the user’s own interests. Extensive experiments on four datasets demonstrate that ConFM consistently outperforms strong baselines, and it also achieves notable improvements on long-tail items.