Incorporating multi‐interest into recommendation with graph convolution networks

International Journal of Intelligent SystemsEarly View RESEARCH ARTICLE Incorporating multi-interest into recommendation with graph convolution networks Nan Jiang, Corresponding Author Nan Jiang jiangnan@ecjtu.edu.cn orcid.org/0000-0003-1712-1872 College of Information Engineering, East China Jiaotong University, Nanchang, P. R. China Correspondence Nan Jiang, College of Information Engineering, East China Jiaotong University, 330013 Nanchang, P. R. China. Email: jiangnan@ecjtu.edu.cn and jiangnan1018@gmail.comSearch for more papers by this authorZilin Zeng, Zilin Zeng orcid.org/0000-0003-4678-4317 College of Information Engineering, East China Jiaotong University, Nanchang, P. R. ChinaSearch for more papers by this authorJie Wen, Jie Wen orcid.org/0000-0002-4959-0692 College of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, P. R. ChinaSearch for more papers by this authorJie Zhou, Jie Zhou orcid.org/0000-0002-8064-8055 College of Information Engineering, East China Jiaotong University, Nanchang, P. R. ChinaSearch for more papers by this authorZiyu Liu, Ziyu Liu orcid.org/0000-0001-9961-2418 College of Information Engineering, East China Jiaotong University, Nanchang, P. R. ChinaSearch for more papers by this authorTao Wan, Tao Wan orcid.org/0000-0002-9507-8868 College of Information Engineering, East China Jiaotong University, Nanchang, P. R. ChinaSearch for more papers by this authorXimeng Liu, Ximeng Liu College of Mathematics and Computer Science, Fuzhou University, Fuzhou, P. R. ChinaSearch for more papers by this authorHonglong Chen, Honglong Chen orcid.org/0000-0003-0739-6338 College of Control Science and Engineering, China University of Petroleum, Qingdao, P. R. ChinaSearch for more papers by this author Nan Jiang, Corresponding Author Nan Jiang jiangnan@ecjtu.edu.cn orcid.org/0000-0003-1712-1872 College of Information Engineering, East China Jiaotong University, Nanchang, P. R. China Correspondence Nan Jiang, College of Information Engineering, East China Jiaotong University, 330013 Nanchang, P. R. China. Email: jiangnan@ecjtu.edu.cn and jiangnan1018@gmail.comSearch for more papers by this authorZilin Zeng, Zilin Zeng orcid.org/0000-0003-4678-4317 College of Information Engineering, East China Jiaotong University, Nanchang, P. R. ChinaSearch for more papers by this authorJie Wen, Jie Wen orcid.org/0000-0002-4959-0692 College of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, P. R. ChinaSearch for more papers by this authorJie Zhou, Jie Zhou orcid.org/0000-0002-8064-8055 College of Information Engineering, East China Jiaotong University, Nanchang, P. R. ChinaSearch for more papers by this authorZiyu Liu, Ziyu Liu orcid.org/0000-0001-9961-2418 College of Information Engineering, East China Jiaotong University, Nanchang, P. R. ChinaSearch for more papers by this authorTao Wan, Tao Wan orcid.org/0000-0002-9507-8868 College of Information Engineering, East China Jiaotong University, Nanchang, P. R. ChinaSearch for more papers by this authorXimeng Liu, Ximeng Liu College of Mathematics and Computer Science, Fuzhou University, Fuzhou, P. R. ChinaSearch for more papers by this authorHonglong Chen, Honglong Chen orcid.org/0000-0003-0739-6338 College of Control Science and Engineering, China University of Petroleum, Qingdao, P. R. ChinaSearch for more papers by this author First published: 10 August 2022 https://doi.org/10.1002/int.22986Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract In recent years, the appearance of graph convolutional networks (GCNs) provides a new idea for graph structure data processing. Because of that, they can learn excellent user and item embedding by using cooperative signals of high-order neighbors, and the GCNs technique shows great potential in the recommendation. The common problem with the bulk of GCN-based models is that it appears the situation of performance degradation during the stacking of network layers. The recently proposed IMP-GCN alleviates this problem to some extent. It aims to avoid the influence of downside information from high-order propagation on embedding learning. However, we consider that it ignores the multi-interest factor, in which users may have different interests. In this paper, we present a multi-interest GCN(MI-GCN) model for a recommendation, and it conducts high-order graph convolution operations in three sets of subgraphs. Users with similar interests and the corresponding interaction items belong to the identical subgraph. As for the formation of the subgraph, we adopt two varied clustering methods and the user feature to form a subgraph generation mechanism. This mechanism can generate three groups of differential subgraphs to divide users into multi-interest groups and make subgraph division more reasonable. We carry out massive experiments on three real-world datasets, demonstrating the effectiveness of our model. Experimental results confirm that our presented MI-GCN outperforms the state-of-the-art GCN-based recommendation models. Open Research DATA AVAILABILITY STATEMENT Research data are not shared. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation