Deep Disentangled Collaborative Filtering with Graph Global Information

Abstract

GCN-based collaborative filtering models generate the representation of user nodes and item nodes by aggregating information on user-item interaction bipartite graph,and then predict users' preferences on items.However,they neglect users' different interaction intents and cannot fully explore the relationship between users and items.Existing graph disentangled collaborative filtering models model users' interaction intents,but ignore the global information of interaction graph and the essential features of users and items,causing the incompleteness of representation semantics.Furthermore,disentangled representation learning is inefficient due to the iterative structure of model.To solve these problems,this paper devises a deep disentangled collaborative filtering model incorporating graph global information,which is named as global graph disentangled collaborative filtering(G2DCF).G2DCF builds graph global channel and graph disentangled channel,which learns essential features and intent features,respectively.Meanwhile,by introducing orthogonality constraint and representation independence constraint,G2DCF makes every user-item interaction intent as unique as possible to prevent intent degradation,and raises the independence of representations under different intents,so as to improve the disentanglement effect.Compared with the previous graph collaborative filtering models,G2DCF can more comprehensively describe features of users and items.A number of experiments are conducted on three public datasets,and results show that the proposed method outperforms the comparison methods on multiple metrics.Further,this paper analyzes the representation distributions from independence and uniformity,verifies the disentanglement effect.It also compares the convergence speed to verify the effectiveness.

Keywords

• recommender system|collaborative filtering|disentangled representation learning|graph neural network|global information