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Knowl. Discov. Data"],"published-print":{"date-parts":[[2017,8,31]]},"abstract":"<jats:p>To improve the performance of recommender systems in a practical manner, several hybrid approaches have been developed by considering item ratings and content information simultaneously. However, most of these hybrid approaches make recommendations based on aggregating different recommendation techniques using various strategies, rather than considering joint modeling of the item\u2019s ratings and content, and thus fail to detect many latent factors that could potentially improve the performance of the recommender systems. For this reason, these approaches continue to suffer from data sparsity and do not work well for recommending items to individual users. A few studies try to describe a user\u2019s preference by detecting items\u2019 latent features from content-description texts as compensation for the sparse ratings. Unfortunately, most of these methods are still generally unable to accomplish recommendation tasks well for two reasons: (1) they learn latent factors from text descriptions or user--item ratings independently, rather than combining them together; and (2) influences of latent factors hidden in texts and ratings are not fully explored. In this study, we propose a probabilistic approach that we denote as latent random walk (LRW) based on the combination of an integrated latent topic model and random walk (RW) with the restart method, which can be used to rank items according to expected user preferences by detecting both their explicit and implicit correlative information, in order to recommend top-ranked items to potentially interested users. As presented in this article, the goal of this work is to comprehensively discover latent factors hidden in items\u2019 ratings and content in order to alleviate the data sparsity problem and to improve the performance of recommender systems. The proposed topic model provides a generative probabilistic framework that discovers users\u2019 implicit preferences and items\u2019 latent features simultaneously by exploiting both ratings and item content information. On the basis of this probabilistic framework, RW can predict a user\u2019s preference for unrated items by discovering global latent relations. In order to show the efficiency of the proposed approach, we test LRW and other state-of-the-art methods on three real-world datasets, namely, CAMRa2011, Yahoo!, and APP. The experiments indicate that our approach outperforms all comparative methods and, in addition, that it is less sensitive to the data sparsity problem, thus demonstrating the robustness of LRW for recommendation tasks.<\/jats:p>","DOI":"10.1145\/3003728","type":"journal-article","created":{"date-parts":[[2017,3,13]],"date-time":"2017-03-13T12:25:15Z","timestamp":1489407915000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Recommendations Based on Comprehensively Exploiting the Latent Factors Hidden in Items\u2019 Ratings and Content"],"prefix":"10.1145","volume":"11","author":[{"given":"Shanshan","family":"Feng","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Cao","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"Stanford University, Standford, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiyou","family":"Qian","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,3,10]]},"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\/1557019.1557029"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1718487.1718499"},{"volume-title":"Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI\u201914)","year":"2014","author":"Bao Yang","key":"e_1_2_1_4_1"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2010.07.024"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015394"},{"volume-title":"Proceedings of the 15th National\/10th Conference on Artificial Intelligence\/Innovative Applications of Artificial Intelligence (AAAI\u201998\/IAAI\u201998)","year":"1998","author":"Basu Chumki","key":"e_1_2_1_7_1"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1345448.1345465"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"volume-title":"Proceedings of the 2nd International Workshop on Context-Aware Recommender Systems.","year":"2010","author":"Bogers Toine","key":"e_1_2_1_10_1"},{"volume-title":"Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. 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