DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jeong, B | - |
dc.contributor.author | Lee, J | - |
dc.contributor.author | Cho, H | - |
dc.date.accessioned | 2016-04-01T08:17:41Z | - |
dc.date.available | 2016-04-01T08:17:41Z | - |
dc.date.created | 2009-04-01 | - |
dc.date.issued | 2009-04 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.other | 2009-OAK-0000019705 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/27652 | - |
dc.description.abstract | Memory-based collaborative filtering is the state-of-the-art method in recommender systems and has proven to be successful in various applications. In this paper we develop novel memory-based methods that incorporate the level of a user credit instead of using similarity between users. The user credit is the degree of one's rating reliability that measures how adherently the user rates items as others do. Preliminary simulation results show that the proposed methods outperform the conventional memory-based ones. The methods are effective in a cold-starting problem. (C) 2008 Elsevier Ltd. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.relation.isPartOf | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.subject | Collaborative filtering | - |
dc.subject | Memory-based method | - |
dc.subject | Recommender system | - |
dc.subject | Sparsity | - |
dc.subject | User credit | - |
dc.subject | OF-THE-ART | - |
dc.subject | RECOMMENDER SYSTEMS | - |
dc.subject | CLASSIFICATION | - |
dc.title | User credit-based collaborative filtering | - |
dc.type | Article | - |
dc.contributor.college | 산업경영공학과 | - |
dc.identifier.doi | 10.1016/j.eswa.2008.09.034 | - |
dc.author.google | Jeong, B | - |
dc.author.google | Lee, J | - |
dc.author.google | Cho, H | - |
dc.relation.volume | 36 | - |
dc.relation.issue | 3 | - |
dc.relation.startpage | 7309 | - |
dc.relation.lastpage | 7312 | - |
dc.contributor.id | 10083567 | - |
dc.relation.journal | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCIE | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | EXPERT SYSTEMS WITH APPLICATIONS, v.36, no.3, pp.7309 - 7312 | - |
dc.identifier.wosid | 000263817100189 | - |
dc.date.tcdate | 2019-02-01 | - |
dc.citation.endPage | 7312 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 7309 | - |
dc.citation.title | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.citation.volume | 36 | - |
dc.contributor.affiliatedAuthor | Lee, J | - |
dc.contributor.affiliatedAuthor | Cho, H | - |
dc.identifier.scopusid | 2-s2.0-58349101058 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 13 | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Collaborative filtering | - |
dc.subject.keywordAuthor | Memory-based method | - |
dc.subject.keywordAuthor | Recommender system | - |
dc.subject.keywordAuthor | Sparsity | - |
dc.subject.keywordAuthor | User credit | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
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