Multi-behavioral recommendation optimizes user experiences by providing users with more accurate choices based on their diverse behaviors, such as view, add to cart, and purchase. Current studies on multi-behavioral recommendation mainly explore the connections and differences between multi-behaviors from an implicit perspective. Specifically, they directly model those relations using black-box neural networks. In fact, users' interactions with items under different behaviors are driven by distinct intents. For instance, when users view products, they tend to pay greater attention to information such as ratings and brands. However, when it comes to the purchasing phase, users become more price-conscious. To tackle this challenge and data sparsity problem in the multi-behavioral recommendation, we propose a novel model: Knowledge-Aware Multi-Intent Contrastive Learning (KAMCL) model. This model uses relationships in the knowledge graph to construct intents, aiming to mine the connections between users' multi-behaviors from the perspective of intents to achieve more accurate recommendations. KAMCL is equipped with two contrastive learning schemes to alleviate the data scarcity problem and further enhance user representations. Extensive experiments on three real datasets demonstrate the superiority of our model.

翻译：多行为推荐通过基于用户的不同行为（如浏览、加入购物车和购买）提供更精准的选择，从而优化用户体验。当前多行为推荐研究主要从隐含视角探索多种行为之间的关联与差异，具体而言，它们使用黑盒神经网络直接建模这些关系。事实上，用户在不同行为下与物品的交互是由不同意图驱动的。例如，用户在浏览产品时更关注评分和品牌等信息，而在购买阶段则对价格更加敏感。为解决这一挑战以及多行为推荐中的数据稀疏问题，我们提出了一种新颖模型：知识感知的多意图对比学习（KAMCL）模型。该模型利用知识图谱中的关系构建意图，旨在从意图视角挖掘用户多行为之间的关联，以实现更精准的推荐。KAMCL配备两种对比学习机制，以缓解数据稀缺问题并进一步增强用户表示。在三个真实数据集上的大量实验证明了我们模型的优越性。