A multiview graph collaborative filtering by incorporating homogeneous and heterogeneous signals. Issue 6 (November 2022)

• Record Type: Journal Article
• Authors: Zheng, Jianxing; Chen, Sen; Du, Yongping; Song, Peng
• Is Part Of: Information processing & management. Volume 59:Issue 6(2022)
• Publication Date: 2022-11
• Subjects: Recommender systems -- Attribute embedding -- Neighbor embedding -- Multiview graph collaborative filtering; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2022.103072
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: In the industrial e-commerce recommender systems, the sparsity of user–item interaction limits the improvement of the performance of collaborative filtering recommendation. Some studies have leveraged attribute co-occurrence or similar neighbors to enhance the semantic representation quality of users and items. Previous methods consider collaborative signals of homogeneous type nodes, such as < u s e r, u s e r > → u s e r and < i t e m, i t e m > → i t e m . By exploiting homogeneous and heterogeneous signals of attribute and neighbor views, we design a multiview graph collaborative filtering (MVGCF) network for recommendation. The MVGCF model utilizes both co-occurrence features of various attribute values and collaborative preference of various neighbors to learn the embedding representation of nodes. Experimental results show that the MVGCF is superior to the state-of-the-art models in AUC and logloss metrics by 1.41% and 3.12% for MovieLens 1M dataset, and by 2.35% and 2.31% for BookCrossing dataset. Aiming at the sparse problem with a small amount of interaction records, our findings is that attribute co-occurrence and neighbor collaboration can improve the accuracy and provide a good explanation for e-commerce recommender systems. Highlights: Homogeneous and heterogeneous signals are leveraged for collaborative embedding. The consistency of attribute and neighbor view-specific node embeddings is studied. A multiview graph collaborative filtering framework is designed for CTR prediction. The validity of data sparse recommendation is verified by extensive experiments.