{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:25:19Z","timestamp":1750220719311,"version":"3.41.0"},"reference-count":40,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2020,5,30]],"date-time":"2020-05-30T00:00:00Z","timestamp":1590796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Key Research Institute of Humanities and Social Sciences at Universities","award":["17JJD630006"],"award-info":[{"award-number":["17JJD630006"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["71490724\/71701007"],"award-info":[{"award-number":["71490724\/71701007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2020,8,31]]},"abstract":"<jats:p>Recommender Systems (RSs) provide users with item choices based on their preferences reflected in past interactions and become important tools to alleviate the information overload problem for users. However, in real-world scenarios, the user\u2013item interaction matrix is generally sparse, leading to the poor performance of recommendation methods. To cope with this problem, social information is introduced into these methods in several ways, such as regularization, ensemble, and sampling. However, these strategies to use social information have their limitations. The regularization and ensemble strategies may suffer from the over-smoothing problem, while the sampling-based strategy may be affected by the overfitting problem. To overcome the limitations of the previous efforts, a novel social recommendation model, namely, Social Collaborative Mutual Learning (SCML), is proposed in this article. SCML combines the item-based CF model with the social CF model by two well-designed mutual regularization strategies. The embedding-level mutual regularization forces the user representations in two models to be close, and the output-level mutual regularization matches the distributions of the predictions in two models. Extensive experiments on three public datasets show that SCML significantly outperforms the baseline methods and the proposed mutual regularization strategies can embrace the advantages of the item-based CF model and the social CF model to improve the recommendation performance.<\/jats:p>","DOI":"10.1145\/3387162","type":"journal-article","created":{"date-parts":[[2020,5,30]],"date-time":"2020-05-30T12:25:16Z","timestamp":1590841516000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Social Collaborative Mutual Learning for Item Recommendation"],"prefix":"10.1145","volume":"14","author":[{"given":"Tianyu","family":"Zhu","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Guannan","family":"Liu","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}]},{"given":"Guoqing","family":"Chen","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2020,5,30]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290982"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00018"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313582"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.psych.55.090902.142015"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/308"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2831682"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"volume-title":"Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531","year":"2015","author":"Hinton Geoffrey","key":"e_1_2_1_9_1"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.22"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864736"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/245108.245123"},{"volume-title":"Proceedings of the International Conference on Learning Representations.","author":"Diederik","key":"e_1_2_1_13_1"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186150"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIC.2003.1167344"},{"volume-title":"Modeling buying motives for personalized product bundle recommendation. ACM Transactions on Knowledge Discovery from Data 11, 3","year":"2017","author":"Liu Guannan","key":"e_1_2_1_17_1"},{"volume-title":"Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 203--210","author":"Ma Hao","key":"e_1_2_1_18_1"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/1458082.1458205"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1935826.1935877"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/1297231.1297235"},{"volume-title":"Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. AUAI Press, 452--461","year":"2009","author":"Rendle Steffen","key":"e_1_2_1_22_1"},{"volume-title":"Antoine Chassang, Carlo Gatta, and Yoshua Bengio.","year":"2014","author":"Romero Adriana","key":"e_1_2_1_23_1"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273596"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/371920.372071"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2740908.2742726"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210023"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339574"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-013-0141-9"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220021"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080771"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2983323.2983701"},{"key":"e_1_2_1_34_1","first-page":"1","article-title":"Collaborative neural social recommendation","volume":"99","author":"Wu Le","year":"2018","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems PP"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/2835776.2835837"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/447"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2013.06.009"},{"volume-title":"Proceedings of the 27th International Conference on World Wide Web. 729--739","year":"2018","author":"Yi Tay","key":"e_1_2_1_38_1"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00454"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/2661829.2661998"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3387162","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3387162","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:33:26Z","timestamp":1750199606000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3387162"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,30]]},"references-count":40,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,8,31]]}},"alternative-id":["10.1145\/3387162"],"URL":"https:\/\/doi.org\/10.1145\/3387162","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2020,5,30]]},"assertion":[{"value":"2019-03-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-03-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-05-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}