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The user purchase behaviors are mainly influenced by their intentions (e.g.,buying clothes for decoration, buying brushes for painting, etc.). Modeling auser's latent intention can significantly improve the performance ofrecommendations. Previous works model users' intentions by considering thepredefined label in auxiliary information or introducing stochastic dataaugmentation to learn purposes in the latent space. However, the auxiliaryinformation is sparse and not always available for recommender systems, andintroducing stochastic data augmentation may introduce noise and thus changethe intentions hidden in the sequence. Therefore, leveraging user intentionsfor sequential recommendation (SR) can be challenging because they arefrequently varied and unobserved. In this paper, Intent contrastive learningwith Cross Subsequences for sequential Recommendation (ICSRec) is proposed tomodel users' latent intentions. Specifically, ICSRec first segments a user'ssequential behaviors into multiple subsequences by using a dynamic slidingoperation and takes these subsequences into the encoder to generate therepresentations for the user's intentions. To tackle the problem of no explicitlabels for purposes, ICSRec assumes different subsequences with the same targetitem may represent the same intention and proposes a coarse-grain intentcontrastive learning to push these subsequences closer. Then, fine-grain intentcontrastive learning is mentioned to capture the fine-grain intentions ofsubsequences in sequential behaviors. Extensive experiments conducted on fourreal-world datasets demonstrate the superior performance of the proposed ICSRecmodel compared with baseline methods.

Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation