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Inf. Syst."],"published-print":{"date-parts":[[2018,4,30]]},"abstract":"<jats:p>\n            Cold-start recommendation is one of the most challenging problems in recommender systems. An important approach to cold-start recommendation is to conduct an interview for new users, called the\n            <jats:italic>interview-based approach<\/jats:italic>\n            . Among the interview-based methods, Representative-Based Matrix Factorization (RBMF) [24] provides an effective solution with appealing merits: it represents users over selected representative items, which makes the recommendations highly intuitive and interpretable. However, RBMF only utilizes a global set of representative items to model all users. Such a representation is somehow too strict and may not be flexible enough to capture varying users\u2019 interests. To address this problem, we propose a novel interview-based model to dynamically create meaningful user groups using decision trees and then select local representative items for different groups. A two-round interview is performed for a new user. In the first round, l\n            <jats:sub>1<\/jats:sub>\n            global questions are issued for group division, while in the second round, l\n            <jats:sub>2<\/jats:sub>\n            local-group-specific questions are given to derive local representation. We collect the feedback on the (l\n            <jats:sub>1<\/jats:sub>\n            +l\n            <jats:sub>2<\/jats:sub>\n            ) items to learn the user representations. By putting these steps together, we develop a joint optimization model, named\n            <jats:italic>local representative-based matrix factorization<\/jats:italic>\n            , for new user recommendations. Extensive experiments on three public datasets have demonstrated the effectiveness of the proposed model compared with several competitive baselines.\n          <\/jats:p>","DOI":"10.1145\/3108148","type":"journal-article","created":{"date-parts":[[2017,8,29]],"date-time":"2017-08-29T17:49:18Z","timestamp":1504028958000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":34,"title":["Local Representative-Based Matrix Factorization for Cold-Start Recommendation"],"prefix":"10.1145","volume":"36","author":[{"given":"Lei","family":"Shi","sequence":"first","affiliation":[{"name":"Chinese Academy of Sciences, Beijing"}]},{"given":"Wayne Xin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing"}]},{"given":"Yi-Dong","family":"Shen","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, Beijing"}]}],"member":"320","published-online":{"date-parts":[[2017,8,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2792838.2800183"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2522422"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/361573.361582"},{"volume-title":"Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. 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How to find a good submatrix. Matrix Methods: Theory Algorithms and Applications (2010) 247.  Sergei Goreinov Ivan Oseledets Dmitry Savostyanov Eugene Tyrtyshnikov and Nikolai Zamarashkin. 2010. How to find a good submatrix. Matrix Methods: Theory Algorithms and Applications (2010) 247.","DOI":"10.1142\/9789812836021_0015"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1454008.1454013"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/312624.312682"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/963770.963774"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/1036843.1036877"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401944"},{"key":"e_1_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Yehuda Koren. 2010. Factor in the neighbors: Scalable and accurate collaborative filtering. 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