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In SPGAT, we aim to leverage social perception to solve the cold start effectively for more accurate recommendations. The approach utilizes a multi-layer graph attention network to aggregate user preference features from collaborative knowledge graphs and social perception graphs. By analyzing the social network of a new user, associated friend users can be identified. The interaction data of these friend users is then provided as side information to recommend to the new user. To handle one-to-many and many-to-many relations, we introduce the TransD graph embedding model, which maps different types of relations and entities to different spaces. To optimize the proposed SPGAT, self-adversarial negative sampling is utilized to implement entity and relation embedding and generate negative samples. Experimental results demonstrate that SPGAT has achieved superior performance compared to several advanced methods.<\/jats:p>","DOI":"10.1145\/3665503","type":"journal-article","created":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T04:27:30Z","timestamp":1716265650000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Social Perception with Graph Attention Network for Recommendation"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7191-8674","authenticated-orcid":false,"given":"Jielin","family":"Jiang","sequence":"first","affiliation":[{"name":"Nanjing University of Information Science and Technology","place":["Nanjing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1099-0923","authenticated-orcid":false,"given":"Pengcheng","family":"Guo","sequence":"additional","affiliation":[{"name":"Nanjing University of Information Science and Technology","place":["Nanjing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4879-9803","authenticated-orcid":false,"given":"Xiaolong","family":"Xu","sequence":"additional","affiliation":[{"name":"Nanjing University of Information Science and Technology","place":["Nanjing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4257-2288","authenticated-orcid":false,"given":"Jintao","family":"Wu","sequence":"additional","affiliation":[{"name":"Nanjing University of Information Science and Technology","place":["Nanjing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9883-2750","authenticated-orcid":false,"given":"Yan","family":"Cui","sequence":"additional","affiliation":[{"name":"Nanjing Normal University of Special Education","place":["Nanjing, China"]}]}],"member":"320","published-online":{"date-parts":[[2025,7,29]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2872518.2890466"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3529318"},{"key":"e_1_3_2_4_2","first-page":"2787","article-title":"Translating embeddings for modeling multi-relational data","volume":"26","author":"Bordes Antoine","year":"2013","unstructured":"Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. 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