基于跨视图对比学习的知识感知推荐系统OACSTPCD

The knowledge-aware recommendation(KGR)domain commonly suffers from the problem of supervised signal sparsity,and contrast learning methods are increasingly studied to address this issue.However,existing contrast learning-based KGR models still have the following limitations.First,existing methods failed to suppress the interference information of unnecessary neighbouring nodes in the knowledge graph be-cause graph convolution is used to directly aggregate all neighbouring nodes;Second,focusing only on the global information would lead to ig-noring the fine-grained local features,causing over-smooth issues.In this work,a Knowledge-aware Recommender System with Cross-Views Contrastive Learning(KRSCCL)is proposed to address the aforementioned issues.In the KRSCCL,a relational graph attention network is pro-posed to construct a global view,including user,item and entity nodes.A lightweight graph convolutional network is designed to construct a loc-al view,including user and item nodes,in which local features are emphasized to effectively mitigate the over-smooth problem.Finally,the con-trastive learning mechanism is performed between intra-and inter-graph node pairs of the two views to fully extract KG signals and further optim-ize the user and item representations.Experimental results on three public datasets from different domains demonstrate that the proposed KR-SCCL achieves expected performance improvement on all the three datasets over selective baselines,F1 score improvement on Movielens-1M,Last.FM and Book-crossing are 2.0%,0.3%and 5.1%,respectively.Most importantly,the relational graph attention network can effectively ex-clude the noise during the feature aggregation of complex networks,the local views can optimize the generation of the node representation and al-leviate the over-smooth problem.