{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T02:55:13Z","timestamp":1763348113950,"version":"3.41.0"},"reference-count":55,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2018,1,23]],"date-time":"2018-01-23T00:00:00Z","timestamp":1516665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Natural Science Foundation of Jiangsu Province for Excellent Young Scholar","award":["BK 20160085"],"award-info":[{"award-number":["BK 20160085"]}]},{"DOI":"10.13039\/501100012164","name":"863 program","doi-asserted-by":"crossref","award":["2015AA015406"],"award-info":[{"award-number":["2015AA015406"]}],"id":[{"id":"10.13039\/501100012164","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"crossref","award":["61472183, 61333014, 61672288 and 91646116"],"award-info":[{"award-number":["61472183, 61333014, 61672288 and 91646116"]}],"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":[[2018,4,30]]},"abstract":"<jats:p>\n            Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized items for different users. Latent factors-based collaborative filtering (CF) has become the popular approaches for RSs due to its accuracy and scalability. Recently, online social networks and user-generated content provide diverse sources for recommendation beyond ratings. Although\n            <jats:italic>social matrix factorization<\/jats:italic>\n            (Social MF) and\n            <jats:italic>topic matrix factorization<\/jats:italic>\n            (Topic MF) successfully exploit social relations and item reviews, respectively; both of them ignore some useful information. In this article, we investigate the effective data fusion by combining the aforementioned approaches. First, we propose a novel model\n            <jats:italic>MR3<\/jats:italic>\n            to jointly model three sources of information (i.e., ratings, item reviews, and social relations) effectively for rating prediction by aligning the latent factors and hidden topics. Second, we incorporate the implicit feedback from ratings into the proposed model to enhance its capability and to demonstrate its flexibility. We achieve more accurate rating prediction on real-life datasets over various state-of-the-art methods. Furthermore, we measure the contribution from each of the three data sources and the impact of implicit feedback from ratings, followed by the sensitivity analysis of hyperparameters. Empirical studies demonstrate the effectiveness and efficacy of our proposed model and its extension.\n          <\/jats:p>","DOI":"10.1145\/3127873","type":"journal-article","created":{"date-parts":[[2018,1,23]],"date-time":"2018-01-23T13:26:43Z","timestamp":1516714003000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":34,"title":["Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback"],"prefix":"10.1145","volume":"12","author":[{"given":"Guang-Neng","family":"HU","sequence":"first","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin-Yu","family":"Dai","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng-Yu","family":"Qiu","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Xia","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Li","sequence":"additional","affiliation":[{"name":"Florida International University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu-Jian","family":"Huang","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia-Jun","family":"Chen","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,1,23]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"volume-title":"Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI\u201914)","author":"Bao Y.","key":"e_1_2_1_2_1"},{"volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence. 2--8.","year":"2014","author":"Bao Yang","key":"e_1_2_1_3_1"},{"volume-title":"Proceedings of the KDD Cup and Workshop. 35","year":"2007","author":"Bennett James","key":"e_1_2_1_4_1"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"volume-title":"Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. 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