{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T20:35:35Z","timestamp":1770237335657,"version":"3.49.0"},"reference-count":37,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2019,1,14]],"date-time":"2019-01-14T00:00:00Z","timestamp":1547424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61502307, 61672358"],"award-info":[{"award-number":["61502307, 61672358"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Hong Kong CERG projects","award":["16211214, 16209715, 16244616"],"award-info":[{"award-number":["16211214, 16209715, 16244616"]}]},{"name":"Hong Kong ITF","award":["ITS\/391\/15FX"],"award-info":[{"award-number":["ITS\/391\/15FX"]}]},{"name":"China National Fundamental Research","award":["2014CB340304"],"award-info":[{"award-number":["2014CB340304"]}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"crossref","award":["2016A030313038"],"award-info":[{"award-number":["2016A030313038"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2019,1,31]]},"abstract":"<jats:p>\n            Heterogeneous one-class collaborative filtering is an emerging and important problem in recommender systems, where two different types of one-class feedback, i.e., purchases and browses, are available as input data. The associated challenges include ambiguity of browses, scarcity of purchases, and heterogeneity arising from different feedback. In this article, we propose to model purchases and browses from a new perspective, i.e., users\u2019 roles of mixer, browser and purchaser. Specifically, we design a novel transfer learning solution termed\n            <jats:italic>role-based transfer to rank<\/jats:italic>\n            (RoToR), which contains two variants, i.e., integrative RoToR and sequential RoToR. In integrative RoToR, we leverage browses into the preference learning task of purchases, in which we take each user as a sophisticated customer (i.e.,\n            <jats:italic>mixer<\/jats:italic>\n            ) that is able to take different types of feedback into consideration. In sequential RoToR, we aim to simplify the integrative one by decomposing it into two dependent phases according to a typical shopping process. Furthermore, we instantiate both variants using different preference learning paradigms such as pointwise preference learning and pairwise preference learning. Finally, we conduct extensive empirical studies with various baseline methods on three large public datasets and find that our RoToR can perform significantly more accurate than the state-of-the-art methods.\n          <\/jats:p>","DOI":"10.1145\/3243652","type":"journal-article","created":{"date-parts":[[2019,1,14]],"date-time":"2019-01-14T13:16:39Z","timestamp":1547471799000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Transfer to Rank for Heterogeneous One-Class Collaborative Filtering"],"prefix":"10.1145","volume":"37","author":[{"given":"Weike","family":"Pan","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering and National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Nanshan District, Shenzhen, China"}]},{"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China"}]},{"given":"Wanling","family":"Cai","sequence":"additional","affiliation":[{"name":"Shenzhen University, Nanshan District, Shenzhen, China"}]},{"given":"Yaofeng","family":"Chen","sequence":"additional","affiliation":[{"name":"Shenzhen University, Nanshan District, Shenzhen, China"}]},{"given":"Qing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenzhen University, Nanshan District, Shenzhen, China"}]},{"given":"Xiaogang","family":"Peng","sequence":"additional","affiliation":[{"name":"Shenzhen University, Nanshan District, Shenzhen, China"}]},{"given":"Zhong","family":"Ming","sequence":"additional","affiliation":[{"name":"Shenzhen University, Nanshan District, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2019,1,14]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3106372"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11257-012-9136-x"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3017429"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273521"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/963770.963776"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2528249"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2016.2541160"},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of the Workshop on Distributed Machine Learning and Matrix Computations at NIPS 2014","author":"Johnson Christopher C.","year":"2014","unstructured":"Christopher C. Johnson . 2014 . Logistic matrix factorization for implicit feedback data . In Proceedings of the Workshop on Distributed Machine Learning and Matrix Computations at NIPS 2014 . http:\/\/stanford.edu\/&sim;rezab\/nips2014workshop\/submits\/logmat.pdf. Christopher C. Johnson. 2014. Logistic matrix factorization for implicit feedback data. In Proceedings of the Workshop on Distributed Machine Learning and Matrix Computations at NIPS 2014. http:\/\/stanford.edu\/&sim;rezab\/nips2014workshop\/submits\/logmat.pdf."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487589"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401944"},{"key":"e_1_2_1_14_1","volume-title":"Deep learning. Nature 521 (5","author":"Lecun Yann","year":"2015","unstructured":"Yann Lecun , Yoshua Bengio , and Geoffrey Hinton . 2015. Deep learning. Nature 521 (5 2015 ), 436--444. Yann Lecun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. 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