Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이민수 | * |
dc.date.accessioned | 2016-08-28T11:08:00Z | - |
dc.date.available | 2016-08-28T11:08:00Z | - |
dc.date.issued | 2012 | * |
dc.identifier.isbn | 9781450311564 | * |
dc.identifier.other | OAK-13890 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/229833 | - |
dc.description.abstract | A variety of recommendation methods have been proposed to satisfy the performance and accuracy; however, it is fairly difficult to satisfy both of them because there is a trade-off between them. In this paper, we introduce the notion of category experts and propose the recommendation method by exploiting the ratings of category experts instead of those of the users similar to a target user. We also extend the method that uses both the category preference of a target user and his/her similarity to category experts. We show that our method significantly outperforms the existing methods in terms of performance and accuracy through extensive experiments with real-world data. © 2012 ACM. | * |
dc.description.sponsorship | Special Interest Group on Information Retrieval (ACM SIGIR);ACM SIGWEB | * |
dc.language | English | * |
dc.title | On using category experts for improving the performance and accuracy in recommender systems | * |
dc.type | Conference Paper | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 2355 | * |
dc.relation.lastpage | 2358 | * |
dc.relation.journaltitle | ACM International Conference Proceeding Series | * |
dc.identifier.doi | 10.1145/2396761.2398639 | * |
dc.identifier.scopusid | 2-s2.0-84871056268 | * |
dc.author.google | Hwang W.-S. | * |
dc.author.google | Lee H.-J. | * |
dc.author.google | Kim S.-W. | * |
dc.author.google | Lee M. | * |
dc.contributor.scopusid | 이민수(57195508191) | * |
dc.date.modifydate | 20240322133406 | * |