Cliques of Graph Convolutional Networks for Recommendation

Graph neural networks have become a popular technique for collaborative filtering. However, most related work is based on user-item bipartite graphs, which can generate a large amount of noise due to the broad and elusive interests of users. To address this problem, we propose a novel generalized insertion framework (CGCN) that directly captures cliques in the item-item co-occurrence graph and considers them as the basic units of the user’s higher-order semantics. The method inserts the structural information in these item-item co-occurrence graphs as an insertion module into the original user-item bipartite graph propagation process, thus providing additional useful information to learn better feature representations. By utilizing the strong proximity relationships between different items in these cliques, the method is able to discover the user’s potential higher-order semantics. We experimentally evaluate two improved variants of the framework on three commonly used public datasets, and the results show significant performance improvements. The method is able to better discover users’ latent true intentions and achieve better recommender system performance by introducing clique information in the item-item co-occurrence graph.