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Inf. Syst."],"published-print":{"date-parts":[[2020,4,30]]},"abstract":"<jats:p>\n            The top-\n            <jats:italic>N<\/jats:italic>\n            recommendation problem has been studied extensively. Item-based collaborative filtering recommendation algorithms show promising results for the problem. They predict a user\u2019s preferences by estimating similarities between a target and user-rated items. Top-\n            <jats:italic>N<\/jats:italic>\n            recommendation remains a challenging task in scenarios where there is a lack of preference history for new items. Feature-based Similarity Models (FSMs) address this particular problem by extending item-based collaborative filtering by estimating similarity functions of item features. The quality of the estimated similarity function determines the accuracy of the recommendation. However, existing FSMs only estimate\n            <jats:italic>global<\/jats:italic>\n            similarity functions; i.e., they estimate using preference information across all users. Moreover, the estimated similarity functions are\n            <jats:italic>linear<\/jats:italic>\n            ; hence, they may fail to capture the complex structure underlying item features.\n          <\/jats:p>\n          <jats:p>In this article, we propose to improve FSMs by estimating local similarity functions, where each function is estimated for a subset of like-minded users. To capture global preference patterns, we extend the global similarity function from linear to nonlinear, based on the effectiveness of variational autoencoders. We propose a Bayesian generative model, called the Local Variational Feature-based Similarity Model, to encapsulate local and global similarity functions. We present a variational Expectation Minimization algorithm for efficient approximate inference. Extensive experiments on a large number of real-world datasets demonstrate the effectiveness of our proposed model.<\/jats:p>","DOI":"10.1145\/3372154","type":"journal-article","created":{"date-parts":[[2020,2,11]],"date-time":"2020-02-11T10:50:09Z","timestamp":1581418209000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Local Variational Feature-Based Similarity Models for Recommending Top-\n            <i>N<\/i>\n            New Items"],"prefix":"10.1145","volume":"38","author":[{"given":"Yifan","family":"Chen","sequence":"first","affiliation":[{"name":"University of Amsterdam, Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1029-9280","authenticated-orcid":false,"given":"Yang","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education, Hefei University of Technology, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongzhi","family":"Yin","sequence":"additional","affiliation":[{"name":"University of Queensland, Brisbane, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ilya","family":"Markov","sequence":"additional","affiliation":[{"name":"University of Amsterdam, Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"MAARTEN De","family":"Rijke","sequence":"additional","affiliation":[{"name":"University of Amsterdam, Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,2,11]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557029"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 4th Italian Information Retrieval Workshop (IIR\u201913)","author":"Aiolli Fabio","year":"2013"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3231933"},{"key":"e_1_2_1_4_1","volume-title":"Vlahavas","author":"Banos Evangelos","year":"2006"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2792838.2800196"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2522422"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098170"},{"key":"e_1_2_1_8_1","volume-title":"Proceedings of the 26th International Conference on World Wide Web (WWW\u201917)","author":"Beutel Alex"},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the 7th International Conference on User Modeling (UM\u201999)","author":"Billsus Daniel"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331192"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11257-015-9155-5"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/2503308.2503357"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2911549"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2988450.2988454"},{"key":"e_1_2_1_15_1","volume-title":"Proceedings of the 2018 World Wide Web Conference on World Wide Web (WWW\u201918)","author":"Cheng Zhiyong","year":"1861"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959156"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959185"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220112"},{"key":"e_1_2_1_19_1","volume-title":"Workshop Track Proceedings of the 3rd International Conference on Learning Representations (ICLR\u201915)","author":"Contardo Gabriella","year":"2015"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864721"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-44566-8_25"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/963770.963776"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.5555\/3304222.3304232"},{"key":"e_1_2_1_24_1","first-page":"109","article-title":"Neural semantic personalized ranking for item cold-start recommendation","volume":"20","author":"Ebesu Travis","year":"2017","journal-title":"Journal"},{"key":"e_1_2_1_25_1","article-title":"Active learning strategies for rating elicitation in collaborative filtering: A system-wide perspective","volume":"5","author":"Elahi Mehdi","year":"2014","journal-title":"ACM Trans. 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