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Inf. Syst."],"published-print":{"date-parts":[[2017,4,30]]},"abstract":"<jats:p>In the real-world environment, users have sufficient experience in their focused domains but lack experience in other domains. Recommender systems are very helpful for recommending potentially desirable items to users in unfamiliar domains, and cross-domain collaborative filtering is therefore an important emerging research topic. However, it is inevitable that the cold-start issue will be encountered in unfamiliar domains due to the lack of feedback data. The Bayesian approach shows that priors play an important role when there are insufficient data, which implies that recommendation performance can be significantly improved in cold-start domains if informative priors can be provided. Based on this idea, we propose a Weighted Irregular Tensor Factorization (WITF) model to leverage multi-domain feedback data across all users to learn the cross-domain priors w.r.t. both users and items. The features learned from WITF serve as the informative priors on the latent factors of users and items in terms of weighted matrix factorization models. Moreover, WITF is a unified framework for dealing with both explicit feedback and implicit feedback. To prove the effectiveness of our approach, we studied three typical real-world cases in which a collection of empirical evaluations were conducted on real-world datasets to compare the performance of our model and other state-of-the-art approaches. The results show the superiority of our model over comparison models.<\/jats:p>","DOI":"10.1145\/2976737","type":"journal-article","created":{"date-parts":[[2016,12,13]],"date-time":"2016-12-13T13:29:02Z","timestamp":1481635742000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Learning Informative Priors from Heterogeneous Domains to Improve Recommendation in Cold-Start User Domains"],"prefix":"10.1145","volume":"35","author":[{"given":"Liang","family":"Hu","sequence":"first","affiliation":[{"name":"University of Technology, Sydney and Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Longbing","family":"Cao","sequence":"additional","affiliation":[{"name":"University of Technology, Sydney, Australia"}]},{"given":"Jian","family":"Cao","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Zhiping","family":"Gu","sequence":"additional","affiliation":[{"name":"Shanghai Technical Institute of Electronics 8 Information, Shanghai, China"}]},{"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Technology, Sydney, Australia"}]},{"given":"Dingyu","family":"Yang","sequence":"additional","affiliation":[{"name":"Shanghai Dian Ji University, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2016,12,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553454"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390334.1390351"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 12th SIAM International Conference on Data Mining","author":"Long M.","year":"2012","unstructured":"M. Long , J. Wang , G. Ding , W. Cheng , X. Zhang , and W. Wang . 2012. Dual transfer learning . In Proceedings of the 12th SIAM International Conference on Data Mining 2012 , 540--551. M. Long, J. Wang, G. Ding, W. Cheng, X. Zhang, and W. Wang. 2012. Dual transfer learning. In Proceedings of the 12th SIAM International Conference on Data Mining 2012, 540--551."},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","unstructured":"B. Loni Y. Shi M. Larson and A. Hanjalic. 2014. Cross-domain collaborative filtering with factorization machines. In Advances in Information Retrieval M. De Rijke T. Kenter A. de Vries C. Zhai F. de Jong K. Radinsky and K. Hofmann (Eds.). Springer International Publishing 656--661.  B. Loni Y. Shi M. Larson and A. Hanjalic. 2014. Cross-domain collaborative filtering with factorization machines. In Advances in Information Retrieval M. De Rijke T. Kenter A. de Vries C. Zhai F. de Jong K. Radinsky and K. Hofmann (Eds.). 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Asuncion , and M. Welling . 2010. Bayesian matrix factorization with side information and dirichlet process mixtures . In AAAI 2010 . I. Porteous, A. U. Asuncion, and M. Welling. 2010. Bayesian matrix factorization with side information and dirichlet process mixtures. In AAAI 2010."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.127"},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (Montreal","author":"Rendle S.","unstructured":"S. Rendle , C. Freudenthaler , Z. Gantner , and L. Schmidt-Thieme . 2009a. BPR: Bayesian personalized ranking from implicit feedback . In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (Montreal , Quebec, Canada. AUAI Press, 1795167, 452--461. S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. 2009a. BPR: Bayesian personalized ranking from implicit feedback. 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