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.

翻译：用户购买行为主要受其意图影响（例如，为装饰购买衣服，为绘画购买画笔等）。对用户的潜在意图进行建模可以显著提升推荐系统的性能。现有工作通过利用辅助信息中的预定义标签或引入随机数据增强以在隐空间学习意图来建模用户意图。然而，辅助信息在推荐系统中往往稀疏且并非始终可得，而引入随机数据增强可能引入噪声，进而改变序列中隐含的意图。因此，由于用户意图频繁变化且不可观测，利用用户意图进行序列推荐（SR）具有挑战性。本文提出基于交叉子序列的意图对比学习用于序列推荐（ICSRec）以建模用户的潜在意图。具体而言，ICSRec首先通过动态滑动操作将用户的序列行为分割为多个子序列，并将这些子序列输入编码器以生成用户意图的表示。为解决意图无显式标签的问题，ICSRec假设具有相同目标物品的不同子序列可能代表相同意图，并提出粗粒度意图对比学习以拉近这些子序列的表示距离。此外，为捕获序列行为中子序列的细粒度意图，进一步提出细粒度意图对比学习。在四个真实数据集上的大量实验表明，所提出的ICSRec模型相较于基线方法具有更优性能。