{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T16:09:31Z","timestamp":1726848571491},"reference-count":31,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2014,12,15]],"date-time":"2014-12-15T00:00:00Z","timestamp":1418601600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2015,1,23]]},"abstract":"\n Recent advances in mobile devices and their sensing capabilities have enabled the collection of rich contextual information and mobile device usage records through the device logs. These context-rich logs open a venue for mining the personal preferences of mobile users under varying contexts and thus enabling the development of personalized context-aware recommendation and other related services, such as mobile online advertising. In this article, we illustrate how to extract personal context-aware preferences from the context-rich device logs, or\n context logs<\/jats:italic>\n for short, and exploit these identified preferences for building personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his or her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences based on two different assumptions, namely, context-independent and context-dependent assumptions, which can fit into different application scenarios. Finally, extensive experiments on a real-world dataset show that both approaches are effective and outperform baselines with respect to mining personal context-aware preferences for mobile users.\n <\/jats:p>","DOI":"10.1145\/2532515","type":"journal-article","created":{"date-parts":[[2014,12,16]],"date-time":"2014-12-16T13:39:54Z","timestamp":1418737194000},"page":"1-27","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":88,"title":["Mining Mobile User Preferences for Personalized Context-Aware Recommendation"],"prefix":"10.1145","volume":"5","author":[{"given":"Hengshu","family":"Zhu","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China"}]},{"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China"}]},{"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Rutgers University, Newark, NJ, USA"}]},{"given":"Kuifei","family":"Yu","sequence":"additional","affiliation":[{"name":"Nokia Research Center, Beijing, China"}]},{"given":"Huanhuan","family":"Cao","sequence":"additional","affiliation":[{"name":"Nokia Research Center, Beijing, China"}]},{"given":"Jilei","family":"Tian","sequence":"additional","affiliation":[{"name":"Nokia, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2014,12,15]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/645920.672836"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/860435.860505"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961634.1961640"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.16"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1871437.1871702"},{"key":"e_1_2_1_7_1","volume-title":"AAAI Spring Symposium on Technosocial Predictive Analytics.","author":"Eagle Nathan"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/1813042.1813054"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020568"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0307752101"},{"key":"e_1_2_1_11_1","unstructured":"Gregor Heinrich. 2005. Parameter Estimation for text Analysis. Technical report University of Lipzig (2005). Gregor Heinrich. 2005. Parameter Estimation for text Analysis. Technical report University of Lipzig (2005)."},{"key":"e_1_2_1_12_1","first-page":"357","article-title":"Context-aware recommender systems using data mining techniques","volume":"64","author":"Kim Kyoung Jae","year":"2010","journal-title":"Journal of World Academy of Science, Engineering and Technology"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/1963192.1963226"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864727"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1964897.1964918"},{"key":"e_1_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Dongjoo Lee Sung Park Minsuk Kahng Sangkeun Lee and Sang-goo Lee. 2010. Exploiting contextual information from event logs for personalized recommendation. Computer and Information Science 121--139. Dongjoo Lee Sung Park Minsuk Kahng Sangkeun Lee and Sang-goo Lee. 2010. Exploiting contextual information from event logs for personalized recommendation. 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