融入多影响力与偏好的图对比学习社交推荐算法  

Graph Contrastive Learning Incorporating Multi-influence and Preference for Social Recommendation

作　　者：胡海波 杨丹[1] 聂铁铮 寇月 HU Haibo;YANG Dan;NIE Tiezheng;KOU Yue(School of Computer Science and Software Engineering,University of Science and Technology Liaoning,Anshan,Liaoning 114051,China;School of Computer Science and Engineering,Northeastern University,Shenyang 110169,China)

机构地区：[1]辽宁科技大学计算机与软件工程学院,辽宁鞍山114051 [2]东北大学计算机科学与工程学院,沈阳110169

出　　处：《计算机科学》2024年第7期146-155,共10页Computer Science

基　　金：国家自然科学基金(62072084,62072086);辽宁省教育厅科学研究项目(LJKMZ20220646)

摘　　要：目前,基于图神经网络的社交推荐方法主要对社交信息和交互信息的显式关系和隐式关系进行联合建模,以缓解冷启动问题。尽管这些方法较好地聚合了社交关系和交互关系,但忽略了高阶隐式关系并非对每个用户都有相同的影响,并且监督学习的方法容易受到流行度偏差的影响。此外,这些方法主要聚焦用户和项目之间的协作关系,没有充分利用项目之间的相似关系。因此,文中提出了一种融入多影响力与偏好的图对比学习社交推荐算法(SocGCL)。一方面,引入节点间(用户和项目)融合机制和图间融合机制,并考虑了项目之间的相似关系。节点间融合机制区分图内不同节点对目标节点的不同影响;图间融合机制聚合多种图的节点嵌入表示。另一方面,通过添加随机噪声进行跨层图对比学习,有效缓解了社交推荐的冷启动问题和流行度偏差。在两个真实数据集上进行实验,结果表明,SocGCL优于其他基线方法,有效提高了社交推荐的性能。At present,social recommendation methods based on graph neural network mainly alleviate the cold start problem by jointly modeling the explicit and implicit relationships of social information and interactive information.Although these methods aggregate social relations and user-item interaction relations well,they ignore that the higher-order implicit relations do not have the same impacts on each user.And these supervised methods are susceptible to popularity bias.In addition,these methods mainly focus on the collaborative function between users and items,but do not make full use of the similarity relations between items.Therefore,this paper proposes a social recommendation algorithm(SocGCL)that incorporates multiple influences and prefe-rences into graph contrastive learning.On the one hand,a fusion mechanism for nodes(users and items)and a fusion mechanism for graphs are introduced,taking into account the similarity relations between items.The fusion mechanism for nodes distinguishes the different impacts of different nodes in the graph on the target node,while the fusion mechanism for graphs aggregates the node embedding representations of multiple graphs.On the other hand,by adding random noise for cross-layer graph contrastive learning,the cold start problem and popularity bias of social recommendation can be effectively alleviated.Experimental results on two real-world datasets show that SocGCL outperforms the baselines and effectively improves the performance of social recommendation.

关 键 词：社交推荐 注意力机制 图对比学习 图神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]