A well-designed recommender system can accurately capture the attributes of users and items, reflecting the unique preferences of individuals. Traditional recommendation techniques usually focus on modeling the singular type of behaviors between users and items. However, in many practical recommendation scenarios (e.g., social media, e-commerce), there exist multi-typed interactive behaviors in user-item relationships, such as click, tag-as-favorite, and purchase in online shopping platforms. Thus, how to make full use of multi-behavior information for recommendation is of great importance to the existing system, which presents challenges in two aspects that need to be explored: (1) Utilizing users' personalized preferences to capture multi-behavioral dependencies; (2) Dealing with the insufficient recommendation caused by sparse supervision signal for target behavior. In this work, we propose a Knowledge Enhancement Multi-Behavior Contrastive Learning Recommendation (KMCLR) framework, including two Contrastive Learning tasks and three functional modules to tackle the above challenges, respectively. In particular, we design the multi-behavior learning module to extract users' personalized behavior information for user-embedding enhancement, and utilize knowledge graph in the knowledge enhancement module to derive more robust knowledge-aware representations for items. In addition, in the optimization stage, we model the coarse-grained commonalities and the fine-grained differences between multi-behavior of users to further improve the recommendation effect. Extensive experiments and ablation tests on the three real-world datasets indicate our KMCLR outperforms various state-of-the-art recommendation methods and verify the effectiveness of our method.

翻译：精心设计的推荐系统能够精准捕捉用户与物品的属性，反映个体独特的偏好。传统推荐技术通常聚焦于建模用户与物品间的单一类型行为。然而，在许多实际推荐场景（如社交媒体、电子商务）中，用户-物品关系存在多种类型的交互行为，例如在线购物平台中的点击、收藏和购买。因此，如何充分利用多行为信息进行推荐对现有系统具有重要意义，这需要从两个方面探讨相关挑战：（1）利用用户个性化偏好捕捉多行为依赖关系；（2）处理由目标行为监督信号稀疏导致的推荐不足问题。本文提出一种知识增强的多行为对比学习推荐框架（KMCLR），包含两个对比学习任务和三个功能模块，分别应对上述挑战。具体而言，我们设计多行为学习模块以提取用户个性化行为信息用于用户嵌入增强，并在知识增强模块中利用知识图谱为物品获取更稳健的知识感知表示。此外，在优化阶段，我们建模用户多行为间的粗粒度共性和细粒度差异，以进一步提升推荐效果。在三个真实数据集上的大量实验和消融测试表明，我们的KMCLR方法优于多种当前最优推荐方法，并验证了其有效性。