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In order to overcome this issue, recommender systems are used in\n                    webshops to provide personalized product recommendations to customers. Though,\n                    model-based recommender systems are not able to provide recommendations to new\n                    customers (i.e., cold users). To facilitate recommendations to cold users we\n                    investigate multiple active learning strategies, and subsequently evaluate which\n                    active learning strategy is able to optimally elicit the preferences from the\n                    cold users in a matrix factorization context. Our model is empirically validated\n                    using a dataset from the webshop of de Bijenkorf, a Dutch department store. We\n                    find that the overall best-performing active learning strategy is PopError, an\n                    active learning strategy that measures the variance score for each item.<\/jats:p>","DOI":"10.3233\/web-210448","type":"journal-article","created":{"date-parts":[[2021,3,26]],"date-time":"2021-03-26T13:54:59Z","timestamp":1616766899000},"page":"269-283","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Active learning strategies for solving the cold user problem in                    model-based recommender systems"],"prefix":"10.1177","volume":"18","author":[{"given":"Tomas","family":"Geurts","sequence":"first","affiliation":[{"name":"Erasmus University Rotterdam, Burgemeester\r                    Oudlaan 50, 3062 PA, Rotterdam, The Netherlands.\r                        E-mails:\u00a0,\u00a0,\u00a0"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stelios","family":"Giannikis","sequence":"additional","affiliation":[{"name":"Erasmus University Rotterdam, Burgemeester\r                    Oudlaan 50, 3062 PA, Rotterdam, The Netherlands.\r                        E-mails:\u00a0,\u00a0,\u00a0"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Flavius","family":"Frasincar","sequence":"additional","affiliation":[{"name":"Erasmus University Rotterdam, Burgemeester\r                    Oudlaan 50, 3062 PA, Rotterdam, The Netherlands.\r                        E-mails:\u00a0,\u00a0,\u00a0"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2021,3,24]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"e_1_3_1_3_2","unstructured":"C.\u00a0Basu H.\u00a0Hirsh and W.\u00a0Cohen Recommendation as classification: Using social and content-based information in recommendation in: Proceedings of the 15th National Conference on Association for the Advancement of Artificial Intelligence\/Innovative Applications of Artificial Intelligence Conferences (AAAI\/IAAI 1998) AAAI 1998 pp.\u00a0714\u2013720."},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2013.03.012"},{"key":"e_1_3_1_5_2","unstructured":"J.S.\u00a0Breese D.\u00a0Heckerman and C.\u00a0Kadie Empirical analysis of predictive algorithms for collaborative filtering in: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI 1998) Morgan Kaufmann Publishers Inc. 1998 pp.\u00a043\u201352."},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1021240730564"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","unstructured":"S.\u00a0Deerwester S.T.\u00a0Dumais G.W.\u00a0Furnas T.K.\u00a0Landauer and R.\u00a0Harshman Indexing by latent semantic analysis Journal of the American Society for Information Science 41(6) (1990) 391. doi:10.1002\/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9.","DOI":"10.1002\/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9"},{"key":"e_1_3_1_8_2","doi-asserted-by":"crossref","unstructured":"M.\u00a0Elahi F.\u00a0Ricci and N.\u00a0Rubens Active learning in collaborative filtering recommender systems in: Proceedings of the 15th International Conference on Electronic Commerce and Web Technologies (EC-Web 2014) Springer 2014 pp.\u00a0113\u2013124.","DOI":"10.1007\/978-3-319-10491-1_12"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","unstructured":"T.\u00a0Geurts and F.\u00a0Frasincar Addressing the cold user problem for model-based recommender systems in: Proceedings of the International Conference on Web Intelligence (WI 2017) ACM 2017 pp.\u00a0745\u2013752. doi:10.1145\/3106426.3106431.","DOI":"10.1145\/3106426.3106431"},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","unstructured":"N.\u00a0Golbandi Y.\u00a0Koren and R.\u00a0Lempel On bootstrapping recommender systems in: Proceedings of the 19th ACM Conference on Information and Knowledge Management (CIKM 2010) ACM 2010 pp.\u00a01805\u20131808.","DOI":"10.1145\/1871437.1871734"},{"key":"e_1_3_1_11_2","doi-asserted-by":"crossref","unstructured":"J.\u00a0Golbeck J.\u00a0Hendler et al. 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