{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T19:51:23Z","timestamp":1774295483684,"version":"3.50.1"},"reference-count":41,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2017,6,30]],"date-time":"2017-06-30T00:00:00Z","timestamp":1498780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Hong Kong CERG","award":["16211214, 16209715 and 16244616"],"award-info":[{"award-number":["16211214, 16209715 and 16244616"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61502307 and 61672358"],"award-info":[{"award-number":["61502307 and 61672358"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"crossref","award":["2014A030310268 and 2016A030313038"],"award-info":[{"award-number":["2014A030310268 and 2016A030313038"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"crossref"}]},{"name":"China National 973 project","award":["2014CB340304"],"award-info":[{"award-number":["2014CB340304"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2017,9,30]]},"abstract":"<jats:p>Intelligent recommendation has been well recognized as one of the major approaches to address the information overload problem in the big data era. A typical intelligent recommendation engine usually consists of three major components, that is, data as the main input, algorithms for preference learning, and system for user interaction and high-performance computation. We observe that the data (e.g., users\u2019 behavior) are usually in different forms, such as examinations (e.g., browse and collection) and ratings, where the former are often much more abundant than the latter. Although the data are in different representations, they are both related to users\u2019 true preferences and are also deemed complementary to each other for preference learning. However, very few ranking or recommendation algorithms have been developed to exploit such two types of user behavior.<\/jats:p>\n          <jats:p>\n            In this article, we focus on jointly modeling the examination behavior and rating behavior and develop a novel and efficient ranking-oriented recommendation algorithm accordingly. First, we formally define a new recommendation problem termed\n            <jats:italic>behavior ranking<\/jats:italic>\n            , which aims to build a ranking-oriented model by exploiting both the examination behavior and rating behavior. Second, we develop a simple and generic\n            <jats:italic>transfer to rank<\/jats:italic>\n            (ToR) algorithm for behavior ranking, which transfers knowledge of candidate items from a global preference learning task to a local preference learning task. Compared with the previous work on integrating heterogeneous user behavior, our ToR algorithm is the first ranking-oriented solution, which can effectively generate recommendations in a more direct manner than those regression-oriented methods. Extensive empirical studies show that our ToR algorithm performs significantly more accurately than the state-of-the-art methods in most cases. Furthermore, our ToR algorithm is very efficient in terms of the time complexity, which is similar to those for homogeneous user behavior alone.\n          <\/jats:p>","DOI":"10.1145\/3057732","type":"journal-article","created":{"date-parts":[[2017,6,30]],"date-time":"2017-06-30T17:44:59Z","timestamp":1498844699000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Transfer Learning for Behavior Ranking"],"prefix":"10.1145","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6326-9531","authenticated-orcid":false,"given":"Weike","family":"Pan","sequence":"first","affiliation":[{"name":"Shenzhen University, Shenzhen, China"}]},{"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}]},{"given":"Yuchao","family":"Duan","sequence":"additional","affiliation":[{"name":"Shenzhen University, Shenzhen, China"}]},{"given":"Ben","family":"Tan","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}]},{"given":"Zhong","family":"Ming","sequence":"additional","affiliation":[{"name":"Shenzhen University, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2017,6,30]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1718487.1718528"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2124295.2124314"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1526709.1526711"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1341531.1341545"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864721"},{"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":"10.1145\/1869652.1869660"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2700495"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/138859.138867"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1008992.1009079"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.22"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2512208"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487589"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401944"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1721654.1721677"},{"key":"e_1_2_1_16_1","volume-title":"Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI\u201912)","author":"Ling Guang","year":"2012","unstructured":"Guang Ling , Haiqin Yang , Michael R. 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