A graph collaborative filtering model combining prototype comparison and feature filtering
Graph convolutional neural networks have achieved great success in collaborative filtering recommendation systems.However,in real recommendation scenarios,collaborative filtering-based recommendation methods are often affected by sparse data,resulting in poor recommendation accuracy.Plus,existing graph collaborative filtering methods generally suffer from incomplete analysis and utilization of user-item interaction information,e.g.,fail to deal with noise in interaction features,and these lead to unsatisfactory recommendation results.To address the above problems,this paper proposes a graph collaborative filtering method that combines prototype comparison and feature filtering.It captures potential connections between nodes by using the proposed prototype contrastive learning task to enhance the representation of users and items,while removes noise from the interaction information.The results of the experiments on three real datasets show that the method improves the accuracy of recommendations while alleviating the data sparsity problem.
万方数据
维普
