LGCCF: A Linear Graph Convolutional Collaborative Filtering with Social Influence

Abstract

Collaborative filtering (CF) is the dominant technique in personalized recommendation. It models user-item interactions to select the relevant items for a user, and it is widely applied in real recommender systems. Recently, graph convolutional network (GCN) has been incorporated into CF, and it achieves better performance in many recommendation scenarios. However, existing works usually suffer from limited performance due to data sparsity and high computational costs in large user-item graphs. In this paper, we propose a linear graph convolutional CF (LGCCF) framework that incorporates the social influence as side information to help improve recommendation and address the aforementioned issues. Specifically, LGCCF integrates the user-item interactions and the social influence into a unified GCN model to alleviate data sparsity. Furthermore, in the graph convolutional operations of LGCCF, we remove the nonlinear transformations and replace them with linear embedding propagations to overcome training difficulty and improve the recommendation performance. Finally, extensive experiments conducted on two real datasets show that LGCCF consistently outperforms the state-of-the-art recommendation methods.