论文标题
解开社会推荐的对比度学习
Disentangled Contrastive Learning for Social Recommendation
论文作者
论文摘要
社会建议利用社会关系来增强建议的代表性学习。大多数社会推荐模型都将用户互动（协作领域）和社会关系（社会领域）的用户表示统一。但是，这种方法可能无法对用户在两个域中的异质行为模式进行建模，从而损害用户表示的表现力。在这项工作中，为了解决这种局限性，我们为社会建议提出了一个新颖的截然不同的学习框架DCREC。更具体地说，我们建议从项目和社会域中学习分开的用户表示。此外，分离的对比度学习旨在在分开的用户表示之间进行社交建议之间的知识转移。各种现实世界数据集的全面实验证明了我们提出的模型的优越性。
Social recommendations utilize social relations to enhance the representation learning for recommendations. Most social recommendation models unify user representations for the user-item interactions (collaborative domain) and social relations (social domain). However, such an approach may fail to model the users heterogeneous behavior patterns in two domains, impairing the expressiveness of user representations. In this work, to address such limitation, we propose a novel Disentangled contrastive learning framework for social Recommendations DcRec. More specifically, we propose to learn disentangled users representations from the item and social domains. Moreover, disentangled contrastive learning is designed to perform knowledge transfer between disentangled users representations for social recommendations. Comprehensive experiments on various real-world datasets demonstrate the superiority of our proposed model.
