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dc.contributor.author김상욱-
dc.date.accessioned2017-04-04T01:36:34Z-
dc.date.available2017-04-04T01:36:34Z-
dc.date.issued2015-07-
dc.identifier.citationJOURNAL OF INTERNET TECHNOLOGY, v. 16, NO 4, Page. 755-765en_US
dc.identifier.issn1607-9264-
dc.identifier.issn2079-4029-
dc.identifier.urihttp://www.airitilibrary.com/Publication/alDetailedMesh?DocID=16079264-201507-201508070022-201508070022-755-765-
dc.identifier.urihttp://hdl.handle.net/20.500.11754/26566-
dc.description.abstractIn a trust network, two users connected by a trust relationship are assumed to have similar interests. Based on this assumption, existing trust-based recommendation methods predict ratings for a target user on unseen items by utilizing available ratings information of those users who are "similar" to the target user in the trust network. Here, the concept of similarity is defined as the reachability via the trust relationship in the network. In the process, however, these methods usually follow the trustor-trustee relationship only in the forward direction, i.e., from trustor to trustee, but not in the backward direction. That is, they have overlooked the possibility of utilizing the ratings information obtainable from those users reachable in the backward direction who, we believe, are also deemed to have similar interests to the target user. In this paper, we investigate this possibility of identifying and adding these users to the trustable user group when predicting ratings by collaborative filtering. Experiments show that our approach can improve the coverage of prediction while preserving the accuracy.en_US
dc.description.sponsorshipThis work was supported by (1) the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2014R1A2A1A10054151),(2) MSIP (Ministry of Science, ICT, and Future Planning)under the ITRC(Information Technology ResearchCenter) support program (NIPA-2014-H0301-14-1022)supervised by the NIPA (National IT Industry Promotion Agency), and (3) Business for Cooperative R&D between Industry, Academy, and Research Institute funded by Korea Small and Medium Business Administration in 2014 (No.C0191469).en_US
dc.language.isoenen_US
dc.publisherNATL ILAN UNIVen_US
dc.subjectRecommendation systemen_US
dc.subjectCollaborative filteringen_US
dc.subjectTrust networken_US
dc.subjectPerformance evaluationen_US
dc.titleOn Exploiting Trustors in Trust-Based Recommendationen_US
dc.typeArticleen_US
dc.relation.no4-
dc.relation.volume16-
dc.identifier.doi10.6138/JIT.2015.16.4.20150310c-
dc.relation.page755-765-
dc.relation.journalJOURNAL OF INTERNET TECHNOLOGY-
dc.contributor.googleauthorHwang, Won-Seok-
dc.contributor.googleauthorLi, Shaoyu-
dc.contributor.googleauthorKim, Sang-Wook-
dc.contributor.googleauthorChoi, Ho Jin-
dc.relation.code2015007327-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDEPARTMENT OF COMPUTER SCIENCE-
dc.identifier.pidwook-
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COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE AND ENGINEERING(컴퓨터공학부) > Articles
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