项目邻居信息对比增强的推荐方法OACSTPCD

Aiming at the weak user-item interaction supervision signal in the knowledge graph-based recommendation method and the problem that the knowledge graph contains noise information,in this paper,we propose a recommendation method for contras-tive enhancement of item neighbor information(RMCEIN).The RMCEIN obtains the multi-order neighbor embedding of users and items through heterogeneous propagation and knowledge-aware attention function,which is used to enrich the characteristics of us-ers and items;in the process of item neighbor embedding,it adopts the method of adding uniformly distributed weak noise to con-struct item neighbor enhancement view,which can effectively reduce the time overhead of view construction.In addition,through contrastive learning between two item neighbor views,the contrastive loss function is called to promote the uniformity of item view information,adjust the neighbor structure of items,achieve the purpose of reducing knowledge noise in the knowledge graph,and at the same time introduce multi-task learning to alleviate the supervision signal weak problem.In order to verify the effectiveness of the method,experiments were carried out on the MovieLens-1M,Book-Crossing and Last-FM datasets,and the experimental results were compared with 10 methods such as RippleNet(Propagating User Preferences on the Knewledge Graph for Recommender Sys-tems),CKAN,KGIC,etc.The AUC(Area Under Curve)of the method in this paper increased by 2.32％on average.F1 value in-creased by 2.26 on average.