Joint Locality Preservation and Adaptive Combination for Graph Collaborative Filtering

Zhiqiang Guo, Chaoyang Wang, Zhi Li, Jianjun Li, Guohui Li

Abstract: Due to its powerful representation ability, Graph Convolutional Network (GCN) based collaborative filtering (CF), which treats the interaction of user-items as a bipartite graph, has become the upstart in recommender systems. Nevertheless, existing GCNs based recommendation model only compromisingly exploits the shallow relationship (generally less than 4 layers) to represent the user and item with different number of interactions, which limits their performance. To address this problem, we propose a novel recommendation framework named joint Locality preservation and Adaptive combination for Graph Collaborative Filtering (LaGCF), which contains two components: locality preservation and adaptive combination, where locality preservation explicitly integrates local features with high-order features at each propagation layer for obtaining better performance faster, while adaptive combination adds adaptive weight and identity matrix in aggregation to enhance the representing power of deep-level GCN. Finally, extensive experiments are conducted on four publicly available datasets, and the results demonstrate the effectiveness and superior performance of our model from both analytical and empirical perspectives.