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In this paper, we propose a Graph Neural Network Social Recommendation Based on Coupled Influence by analyzing the social influence of 2-level friends (CI-GNNSR). First, we mine the user\u2019s historical rating information and second-degree social information. Then, to learn the feature representation of users and movies, multiple Graph Attention Networks (GAT) are used to model the user-movie Graph and social network Graph. Our algorithm uses an attention-based memory network to learn the interest influence representation between users and their collaborative friends, which can distinguish the related factors among different users\u2019 friends. The experiment results show that CI-GNNSR enhances the accuracy of recommendation by considering users\u2019 social influence factors from multiple perspectives. <\/jats:p>","DOI":"10.1142\/s0218001422510168","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T07:14:11Z","timestamp":1664435651000},"source":"Crossref","is-referenced-by-count":3,"title":["Design of Graph Neural Network Social Recommendation Algorithm Based on Coupling Influence"],"prefix":"10.1142","volume":"36","author":[{"given":"Wei","family":"Qi","sequence":"first","affiliation":[{"name":"Jiangsu Union Technical Institute, Xuzhou, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxu","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, P. R. 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