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Knowl. Discov. Data"],"published-print":{"date-parts":[[2018,12,31]]},"abstract":"<jats:p>\n            Recently,\n            <jats:italic>latent factor model<\/jats:italic>\n            (LFM) has been drawing much attention in recommender systems due to its good performance and scalability. However, existing LFMs predict missing values in a user-item rating matrix only based on the known ones, and thus the\n            <jats:italic>sparsity<\/jats:italic>\n            of the rating matrix always limits their performance. Meanwhile,\n            <jats:italic>semi-supervised learning<\/jats:italic>\n            (SSL) provides an effective way to alleviate the label (i.e., rating) sparsity problem by performing label propagation, which is mainly based on the\n            <jats:italic>smoothness<\/jats:italic>\n            insight on affinity graphs. However, graph-based SSL suffers serious scalability and graph unreliable problems when directly being applied to do recommendation. In this article, we propose a novel probabilistic\n            <jats:italic>chain graph model<\/jats:italic>\n            (CGM) to marry SSL with LFM. The proposed CGM is a combination of\n            <jats:italic>Bayesian network<\/jats:italic>\n            and\n            <jats:italic>Markov random field<\/jats:italic>\n            . The Bayesian network is used to model the rating generation and regression procedures, and the Markov random field is used to model the confidence-aware smoothness constraint between the generated ratings. Experimental results show that our proposed CGM significantly outperforms the state-of-the-art approaches in terms of four evaluation metrics, and with a larger performance margin when data sparsity increases.\n          <\/jats:p>","DOI":"10.1145\/3264745","type":"journal-article","created":{"date-parts":[[2018,10,2]],"date-time":"2018-10-02T12:09:22Z","timestamp":1538482162000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Semi-supervised Learning Meets Factorization"],"prefix":"10.1145","volume":"12","author":[{"given":"Chaochao","family":"Chen","sequence":"first","affiliation":[{"name":"Zhejiang University and University of Illinois at Urbana-Champaign"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin Chen-Chuan","family":"Chang","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qibing","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5483-0366","authenticated-orcid":false,"given":"Xiaolin","family":"Zheng","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,10]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557029"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1718487.1718499"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of KDD Cup and Workshop 2007 Aug 12","volume":"2007","author":"Bennett James","year":"2007","unstructured":"James Bennett and Stan Lanning . 2007 . 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