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，为装饰购买衣物、为绘画购买刷子等）。对用户潜在意图进行建模可以显著提升推荐性能。现有工作通过考虑辅助信息中的预定义标签或引入随机数据增强来学习潜在空间中的目的，从而对用户意图进行建模。然而，辅助信息稀疏且推荐系统中并非总是可用，而引入随机数据增强可能会引入噪声，从而改变序列中隐藏的意图。因此，利用用户意图进行序列推荐具有挑战性，因为意图通常多变且不可观测。本文提出面向序列推荐的意图跨子序列对比学习（ICSRec）模型，用于对用户潜在意图进行建模。具体而言，ICSRec首先通过动态滑动操作将用户序列行为切分为多个子序列，并将这些子序列输入编码器以生成用户意图表征。为解决意图无显式标签的问题，ICSRec假设具有相同目标项目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距离。随后，通过细粒度意图对比学习捕获序列行为中子序列的细粒度意图。在四个真实数据集上进行的广泛实验表明，所提出的ICSRec模型相比基线方法具有优越性能。