Graph Contrastive Learning Incorporating Multi-influence and Preference for Social Recommendation
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 distingui-shes 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 contras-tive learning,the cold start problem and popularity bias of social recommendation can be effectively alleviated.Experimental re-sults on two real-world datasets show that SocGCL outperforms the baselines and effectively improves the performance of social recommendation.
Social RecommendationAttention MechanismGraph Contrastive LearningGraph Neural Networks
NETL
NSTL
万方数据
