Graph convolutional network combining node similarity association and layer attention for personalized recommendation. (May 2023)

• Record Type: Journal Article
• Authors: Cai, Linqin; Lai, Tingjie; Wang, Lingjun; Zhou, Yanan; Xiong, Yu
• Is Part Of: Engineering applications of artificial intelligence. Volume 121(2023)
• Publication Date: 2023-05
• Subjects: Personalized recommendation -- Graph convolutional network -- Node similarity association -- Layer attention; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2023.105981
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Although current graph convolutional network (GCN) has achieved competitive performance in personalized recommendation systems, most of existing GCN based recommendation methods mainly rely on user–item interaction data and the fixed association weights of the nodes at different layers, which greatly limit them to further effectively learn the final embedding representation of nodes when interaction data is scarce. This paper proposes a GCN based recommendation model combining node similarity association and layer attention mechanism (NSAGCN) for predicting user–item interactions in personalized recommendation. The proposed NSAGCN model integrates the similarity associations of the same type nodes into a heterogeneous network based on the bipartite graph of user–item interaction to enrich semantic information of the original sparse interaction graph and more effectively learn the node embedding features. In addition, a layer attention strategy is used to aggregate the embeddings from different graph convolutional layers with various association weights according to the proximity to the target nodes. Extensive experiments on three public benchmark datasets (ML-100K, ML-1M, and Book-Crossing) show that the proposed NSAGCN model outperforms state-of-the-art models by an average improvement of 8.58%, 6.91%, and 6.33% in Recall, Precision, and Normalized Discounted Cumulative Gain ( NDCG ), respectively.