How to impute missing ratings?: Claims, solution, and its application to collaborative filtering
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Youngnam | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.contributor.author | Park, Sunju | - |
dc.contributor.author | Xie,Xing | - |
dc.date.accessioned | 2022-07-12T00:48:12Z | - |
dc.date.available | 2022-07-12T00:48:12Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2018-04 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/150235 | - |
dc.description.abstract | Data sparsity is one of the biggest problems faced by collaborative filtering used in recommender systems. Data imputation alleviates the data sparsity problem by inferring missing ratings and imputing them to the original rating matrix. In this paper, we identify the limitations of existing data imputation approaches and suggest three new claims that all data imputation approaches should follow to achieve high recommendation accuracy. Furthermore, we propose a deep-learning based approach to compute imputed values that satisfies all three claims. Based on our hypothesis that most pre-use preferences (e.g., impressions) on items lead to their post-use preferences (e.g., ratings), our approach tries to understand via deep learning how pre-use preferences lead to post-use preferences differently depending on the characteristics of users and items. Through extensive experiments on real-world datasets, we verify our three claims and hypothesis, and also demonstrate that our approach significantly outperforms existing state-of-the-art approaches. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.title | How to impute missing ratings?: Claims, solution, and its application to collaborative filtering | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.identifier.doi | 10.1145/3178876.3186159 | - |
dc.identifier.scopusid | 2-s2.0-85058268235 | - |
dc.identifier.bibliographicCitation | The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018, pp.783 - 792 | - |
dc.relation.isPartOf | The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018 | - |
dc.citation.title | The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018 | - |
dc.citation.startPage | 783 | - |
dc.citation.endPage | 792 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | World Wide Web | - |
dc.subject.keywordPlus | Data imputation | - |
dc.subject.keywordPlus | Data sparsity | - |
dc.subject.keywordPlus | Data sparsity problems | - |
dc.subject.keywordPlus | ITS applications | - |
dc.subject.keywordPlus | Learning-based approach | - |
dc.subject.keywordPlus | Real-world datasets | - |
dc.subject.keywordPlus | Recommendation accuracy | - |
dc.subject.keywordPlus | State-of-the-art approach | - |
dc.subject.keywordPlus | Collaborative filtering | - |
dc.subject.keywordAuthor | Collaborative filtering | - |
dc.subject.keywordAuthor | Data imputation | - |
dc.subject.keywordAuthor | Data sparsity | - |
dc.subject.keywordAuthor | Recommender systems | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/3178876.3186159 | - |
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.