Sequential recommendation is one of the important branches of recommender system, aiming to achieve personalized recommended items for the future through the analysis and prediction of users' ordered historical interactive behaviors. However, along with the growth of the user volume and the increasingly rich behavioral information, how to understand and disentangle the user's interactive multi-intention effectively also poses challenges to behavior prediction and sequential recommendation. In light of these challenges, we propose a Contrastive Learning sequential recommendation method based on Multi-Intention Disentanglement (MIDCL). In our work, intentions are recognized as dynamic and diverse, and user behaviors are often driven by current multi-intentions, which means that the model needs to not only mine the most relevant implicit intention for each user, but also impair the influence from irrelevant intentions. Therefore, we choose Variational Auto-Encoder (VAE) to realize the disentanglement of users' multi-intentions. We propose two types of contrastive learning paradigms for finding the most relevant user's interactive intention, and maximizing the mutual information of positive sample pairs, respectively. Experimental results show that MIDCL not only has significant superiority over most existing baseline methods, but also brings a more interpretable case to the research about intention-based prediction and recommendation.

翻译：序列推荐是推荐系统的重要分支之一，旨在通过分析与预测用户有序的历史交互行为，实现面向未来的个性化推荐项目。然而，随着用户数量的增长及行为信息的日益丰富，如何有效理解并解耦用户交互中的多重意图，对行为预测及序列推荐也带来了挑战。针对这些挑战，我们提出了一种基于多意图解耦的对比学习序列推荐方法（MIDCL）。在本工作中，意图被视为动态且多样的，用户行为往往由当前的多重意图驱动，这意味着模型不仅需要挖掘每个用户最相关的隐含意图，还需削弱无关意图的影响。因此，我们选择变分自编码器（VAE）来实现用户多意图的解耦。我们提出了两种对比学习范式，分别用于寻找最相关的用户交互意图，以及最大化正样本对的互信息。实验结果表明，MIDCL不仅显著优于大多数现有基线方法，还为目标基于意图的预测与推荐研究提供了更具可解释性的案例。