A Graph Convolutional Neural Network for Recommendation Based on Community Detection and Combination of Multiple Heterogeneous Graphs

Graph Convolutional Neural Networks (GCNs) have performed well in many recommendation scenarios. In spite of this, recommendation models based on GCNs still face problems such as insufficient information mining and high complexity for some existing models. To address the above problems, we propose a Graph Convolutional Neural Network for Recommendation Based on Community Detection and the Combination of Multiple Heterogeneous Graphs (GCN-CMHG). This model uses the community detection algorithm to detect the communities in the user-item interaction heterogeneous graph (UIIHG), Finds the regional central nodes of communities, and then creates edges between the regional central node of each community and all other nodes in the UIIHG to construct the heterogeneous partial adjacent graph. Then, a Heterogeneous Partial Adjacent Auxiliary (HPAA) layer is designed to aggregate information on the heterogeneous partial adjacent graph. HPAA layer expands the influence of distant nodes on target nodes, enables target nodes to receive global information, and enhances the ability of GCN-CMHG to mine information. Specially, due to the low complexity of HPAA layer and the abandonment of redundant information, GCN-CMHG is easier to implement and train. Under the exact same experimental setting, GCN-CMHG's time consumption is only about 1/10 of another model based on GCN called Graph Convolutional Neural Network for Recommendation Based on the Combination of Multiple Heterogeneous Graphs (GCN-MHG). Experiments on multiple real-world datasets show that GCN-CMHG achieves better results compared with several advanced models. The implementation of our work can be found at https://github.com/GCNRSs/GCN-CMHG.