{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:56:34Z","timestamp":1760709394499,"version":"3.41.0"},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2017,8,21]],"date-time":"2017-08-21T00:00:00Z","timestamp":1503273600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Web"],"published-print":{"date-parts":[[2018,2,28]]},"abstract":"<jats:p>\n            Users\u2019 preferences, and consequently their ratings and reviews to items, change over time. Likewise, characteristics of items are also time-varying. By dividing data into time periods, temporal Recommender Systems (RSs) improve recommendation accuracy by exploring the temporal dynamics in user rating data. However, temporal RSs have to cope with rating sparsity in each time period. Meanwhile, reviews generated by users contain rich information about their preferences, which can be exploited to address rating sparsity and further improve the performance of temporal RSs. In this article, we develop a temporal rating model with topics that jointly mines the temporal dynamics of both user-item ratings and reviews. Studying temporal drifts in reviews helps us understand item rating evolutions and user interest changes over time. Our model also automatically splits the review text in each time period into\n            <jats:italic>interim<\/jats:italic>\n            words and\n            <jats:italic>intrinsic<\/jats:italic>\n            words. By linking interim words and intrinsic words to short-term and long-term item features, respectively, we jointly mine the temporal changes in user and item latent features together with the associated review text in a single learning stage. Through experiments on 28 real-world datasets collected from\n            <jats:italic>Amazon<\/jats:italic>\n            , we show that the rating prediction accuracy of our model significantly outperforms the existing state-of-art RS models. And our model can automatically identify representative interim words in each time period as well as intrinsic words across all time periods. This can be very useful in understanding the time evolution of users\u2019 preferences and items\u2019 characteristics.\n          <\/jats:p>","DOI":"10.1145\/3108238","type":"journal-article","created":{"date-parts":[[2017,8,24]],"date-time":"2017-08-24T11:49:04Z","timestamp":1503575344000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Recommendation in a Changing World"],"prefix":"10.1145","volume":"12","author":[{"given":"Yining","family":"Liu","sequence":"first","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[{"name":"New York University, Brooklyn, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanming","family":"Shen","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keqiu","family":"Li","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,8,21]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2007.90"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11257-012-9136-x"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623758"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2043932.2043951"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0307752101"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806504"},{"volume-title":"Proceedings of the 2010 World Conference on Educational Multimedia, Hypermedia, and Telecommunications. 1028--1033","year":"2010","author":"Hermann Christoph","key":"e_1_2_1_8_1"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/963770.963774"},{"key":"e_1_2_1_10_1","unstructured":"Guang-Neng Hu Xin-Yu Dai Yunya Song Shu-Jian Huang and Jia-Jun Chen. 2016. A synthetic approach for recommendation: Combining ratings social relations and reviews. arXiv:1601.02327 (2016).   Guang-Neng Hu Xin-Yu Dai Yunya Song Shu-Jian Huang and Jia-Jun Chen. 2016. A synthetic approach for recommendation: Combining ratings social relations and reviews. arXiv:1601.02327 (2016)."},{"key":"e_1_2_1_11_1","first-page":"99","article-title":"Time based tag recommendation using direct and extended users sets","volume":"209","author":"Iofciu Tereza","year":"2009","journal-title":"ECML PKDD Disc. Chall."},{"key":"e_1_2_1_12_1","unstructured":"Paul B. Kantor Lior Rokach Francesco Ricci and Bracha Shapira. 2011. Recommender Systems Handbook. Springer.  Paul B. Kantor Lior Rokach Francesco Ricci and Bracha Shapira. 2011. Recommender Systems Handbook. 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Collaborative filtering and the missing at random assumption. arXiv:1206.5267 (2012)."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339577"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2507157.2507163"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2488388.2488466"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242596"},{"key":"e_1_2_1_23_1","unstructured":"Andriy Mnih and Ruslan Salakhutdinov. 2007. Probabilistic matrix factorization. In Advances in Neural Information Processing Systems. 1257--1264.   Andriy Mnih and Ruslan Salakhutdinov. 2007. Probabilistic matrix factorization. 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