Using composite attribute similarity multi-graph convolutional network for recommendation

Graph Convolutional Networks (GCNs) are frequently utilized and havel a significant role in recommender systems. This is attributed to their ability to capture signals of collaboration between higher-order neighbors using graph structures. GCN-based recommendation models have been greatly improved in improving recommendation performance, but continue to face serious data sparsity problems. Data sparsity can be effectively alleviated by introducing attribute information. However, current GCN-based models face challenges in effectively handling the diverse attribute information of users and items and capturing the complex relationships among users, items, and attributes. With the purpose of addressing aforementioned problems, this research proposes a Using Composite Attribute Similarity Multi-Graph Convolutional Network (UCASM-GCN) for recommendation. In concrete terms, an attribute fusion strategy based on the attention mechanism is first utilized to construct the composite attributes of users or items. Then, the user-user graph and the item-item graph are constructed using the composite attributes of nodes to mine the relationships between users and between items. Finally, two isomorphic graphs are injected into the user-item interaction graph as auxiliary information through a multi-graph convolution strategy to generate optimized embedding representations, which ultimately improve the recommendation performance. Extensive experiments on three public datasets demonstrate the effectiveness of the proposed UCASM-GCN, achieving performance gains of 2.48%, 8.20% and 5.52% over a competitive graph-based collaborative filtering model on the Movielens 100k, Movielens 1M and DoubanBook datasets, respectively.