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Intell. Syst. Technol."],"published-print":{"date-parts":[[2018,11,30]]},"abstract":"<jats:p>Recommender systems are common in e-commerce platforms in recent years. Recommender systems are able to help users find preferential items among a large amount of products so that users\u2019 time is saved and sellers\u2019 profits are increased. Cross-domain recommender systems aim to recommend items based on users\u2019 different tastes across domains. While recommender systems usually suffer from the user cold-start problem that leads to unsatisfying recommendation performance, cross-domain recommendation can remedy such a problem. This article proposes a novel cross-domain recommendation model based on regression analysis, partial least squares regression (PLSR). The proposed recommendation models, PLSR-CrossRec and PLSR-Latent, are able to purely use source-domain ratings to predict the ratings for cold-start users who never rated items in the target domains. Experiments conducted on the Epinions dataset with ten various domains\u2019 rating records demonstrate that PLSR-Latent can outperform several matrix factorization-based competing methods under a variety of cross-domain settings. The time efficiency of PLSR-Latent is also satisfactory.<\/jats:p>","DOI":"10.1145\/3231601","type":"journal-article","created":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T12:18:14Z","timestamp":1541074694000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["A Cross-Domain Recommendation Mechanism for Cold-Start Users Based on Partial Least Squares Regression"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7995-4787","authenticated-orcid":false,"given":"Cheng-Te","family":"Li","sequence":"first","affiliation":[{"name":"National Cheng Kung University, Tainan City, Taiwan (R.O.C)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chia-Tai","family":"Hsu","sequence":"additional","affiliation":[{"name":"National Chengchi University, Taipei City, Taiwan (R.O.C)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Man-Kwan","family":"Shan","sequence":"additional","affiliation":[{"name":"National Chengchi University, Taipei City, Taiwan (R.O.C)"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,11]]},"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":"10.1002\/job.686"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2013.03.012"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI\u201998)","author":"Breese J. 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