BI-GCN: Bilateral Interactive Graph Convolutional Network for Recommendation

Abstract

Recently, Graph Convolutional Network (GCN) based methods have become novel state-of-the-arts for Collaborative Filtering (CF) based Recommender Systems. To obtain users' preferences over different items, it is a common practice to learn representations of users and items by performing embedding propagation on a user-item bipartite graph, and then calculate the preference scores based on the representations. However, in most existing algorithms, user/item representations are generated independently of target items/users. To address this problem, we propose a novel graph attention model named Bilateral Interactive GCN (BI-GCN), which introduces bilateral interactive guidance into each user-item pair and thus leads to target-aware representations for preference prediction. Specifically, to learn the user/item representation from its neighborhood, we assign higher attention weights to those neighbors similar to the target item/user. By this manner, we can obtain target-aware representations, i.e., the information of the target item/user is explicitly encoded in the corresponding user/item representation, for more precise matching. Extensive experiments on three benchmark datasets demonstrate the effectiveness and robustness of BI-GCN.