In recommender systems, knowledge graph (KG) can offer critical information that is lacking in the original user-item interaction graph (IG). Recent process has explored this direction and shows that contrastive learning is a promising way to integrate both. However, we observe that existing KG-enhanced recommenders struggle in balancing between the two contrastive views of IG and KG, making them sometimes even less effective than simply applying contrastive learning on IG without using KG. In this paper, we propose a new contrastive learning framework for KG-enhanced recommendation. Specifically, to make full use of the knowledge, we construct two separate contrastive views for KG and IG, and maximize their mutual information; to ease the contrastive learning on the two views, we further fuse KG information into IG in a one-direction manner.Extensive experimental results on three real-world datasets demonstrate the effectiveness and efficiency of our method, compared to the state-of-the-art. Our code is available through the anonymous link:https://figshare.com/articles/conference_contribution/SimKGCL/22783382

翻译：在推荐系统中，知识图谱能够提供原始用户-物品交互图所缺失的关键信息。最近的研究进展表明，对比学习是融合两者的有效途径。然而，我们观察到现有知识图谱增强的推荐模型在平衡交互图和知识图谱的对比视图时存在困难，有时甚至导致其效果不如仅在交互图上应用对比学习而忽略知识图谱的方法。本文提出了一种面向知识图谱增强推荐的新对比学习框架。具体而言，为充分利用知识信息，我们分别为知识图谱和交互图构建了两个独立的对比视图，并最大化它们的互信息；为简化两个视图上的对比学习过程，我们进一步以单向方式将知识图谱信息融入交互图。在三个真实数据集上的广泛实验结果表明，与当前最先进方法相比，我们的方法具有显著的有效性和高效性。相关代码可通过匿名链接获取：https://figshare.com/articles/conference_contribution/SimKGCL/22783382