Data imputation using a trust network for recommendation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hwang, Won-Seok | - |
dc.contributor.author | Li, Shaoyu | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.contributor.author | Lee, Kichun | - |
dc.date.accessioned | 2022-07-16T05:18:22Z | - |
dc.date.available | 2022-07-16T05:18:22Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2014-04 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/160246 | - |
dc.description.abstract | Recommendation methods suffer from the data sparsity and cold-start user problems, often resulting in low accuracy. To address these problems, we propose a novel imputation method, which effectively densifies a rating matrix by filling unevaluated ratings with probable values. In our method, we use a trust network to estimate the unevaluated ratings accurately. We conduct experiments on the Epinions dataset and demonstrate that our method helps provide better recommendation accuracy than previous methods, especially for cold-start users. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.title | Data imputation using a trust network for recommendation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.identifier.doi | 10.1145/2567948.2577363 | - |
dc.identifier.scopusid | 2-s2.0-84990914023 | - |
dc.identifier.bibliographicCitation | WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web, pp.299 - 300 | - |
dc.relation.isPartOf | WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web | - |
dc.citation.title | WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web | - |
dc.citation.startPage | 299 | - |
dc.citation.endPage | 300 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Factorization | - |
dc.subject.keywordPlus | Recommender systems | - |
dc.subject.keywordPlus | World Wide Web | - |
dc.subject.keywordPlus | Data imputation | - |
dc.subject.keywordPlus | Data sparsity | - |
dc.subject.keywordPlus | Imputation methods | - |
dc.subject.keywordPlus | Matrix factorizations | - |
dc.subject.keywordPlus | Recommendation accuracy | - |
dc.subject.keywordPlus | Recommendation methods | - |
dc.subject.keywordPlus | Trust networks | - |
dc.subject.keywordPlus | User problems | - |
dc.subject.keywordPlus | Matrix algebra | - |
dc.subject.keywordAuthor | Data imputation | - |
dc.subject.keywordAuthor | Matrix factorization | - |
dc.subject.keywordAuthor | Recommendation system | - |
dc.subject.keywordAuthor | Trust network | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/2567948.2577363 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.