  The user purchase behaviors are mainly influenced by their intentions (e.g.,
buying clothes for decoration, buying brushes for painting, etc.). Modeling a
user's latent intention can significantly improve the performance of
recommendations. Previous works model users' intentions by considering the
predefined label in auxiliary information or introducing stochastic data
augmentation to learn purposes in the latent space. However, the auxiliary
information is sparse and not always available for recommender systems, and
introducing stochastic data augmentation may introduce noise and thus change
the intentions hidden in the sequence. Therefore, leveraging user intentions
for sequential recommendation (SR) can be challenging because they are
frequently varied and unobserved. In this paper, Intent contrastive learning
with Cross Subsequences for sequential Recommendation (ICSRec) is proposed to
model users' latent intentions. Specifically, ICSRec first segments a user's
sequential behaviors into multiple subsequences by using a dynamic sliding
operation and takes these subsequences into the encoder to generate the
representations for the user's intentions. To tackle the problem of no explicit
labels for purposes, ICSRec assumes different subsequences with the same target
item may represent the same intention and proposes a coarse-grain intent
contrastive learning to push these subsequences closer. Then, fine-grain intent
contrastive learning is mentioned to capture the fine-grain intentions of
subsequences in sequential behaviors. Extensive experiments conducted on four
real-world datasets demonstrate the superior performance of the proposed ICSRec
model compared with baseline methods.
