Chen Rui, Chen Jialu, Gan Xianghua
School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu, 611130, China.
Sci Rep. 2024 Sep 30;14(1):22643. doi: 10.1038/s41598-024-73336-5.
With the rapid popularity of online social media, recommendation systems have increasingly harnessed social relations to enhance user-item interactions and mitigate the data sparsity issue. Beyond social connections, the semantic relatedness among items has emerged as a crucial factor in comprehending their inherent connections. In this work, we propose a novel Multi-view Contrastive learning framework for Social Recommendation, named MultiCSR. This framework adaptively incorporates user social networks and item knowledge graphs into modeling users preferences within recommendation systems. To facilitate the alignment of different views, we introduce a dedicated multi-view contrastive learning process that extracts rich information from each view and foster mutual enhancement. Extensive experiments conducted on three real-world datasets demonstrate the effectiveness of our framework over representative recommendation methods. Furthermore, ablation studies offer a deeper understanding of the mechanisms underlying our framework.
随着在线社交媒体的迅速普及，推荐系统越来越多地利用社会关系来增强用户与物品的交互，并缓解数据稀疏问题。除了社会联系之外，物品之间的语义相关性已成为理解其内在联系的关键因素。在这项工作中，我们提出了一种用于社交推荐的新颖的多视图对比学习框架，称为MultiCSR。该框架将用户社交网络和物品知识图谱自适应地整合到推荐系统中对用户偏好进行建模。为了促进不同视图的对齐，我们引入了一个专门的多视图对比学习过程，该过程从每个视图中提取丰富信息并促进相互增强。在三个真实世界数据集上进行的大量实验证明了我们的框架相对于代表性推荐方法的有效性。此外，消融研究让我们对框架背后的机制有了更深入的理解。
