Rating augmentation with generative adversarial networks towards accurate collaborative filtering
DC Field | Value | Language |
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dc.contributor.author | Chae, Dong-Kyu | - |
dc.contributor.author | Kang, Jin-Soo | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.contributor.author | Choi, Jaeho | - |
dc.date.accessioned | 2022-07-09T15:03:09Z | - |
dc.date.available | 2022-07-09T15:03:09Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2019-05 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/147841 | - |
dc.description.abstract | Generative Adversarial Networks (GAN) have not only achieved a big success in various generation tasks such as images, but also boosted the accuracy of classification tasks by generating additional labeled data, which is called data augmentation. In this paper, we propose a Rating Augmentation framework with GAN, named RAGAN, aiming to alleviate the data sparsity problem in collaborative filtering (CF), eventually improving recommendation accuracy significantly. We identify a unique challenge that arises when applying GAN to CF for rating augmentation: naive RAGAN tends to generate values biased towards high ratings. Then, we propose a refined version of RAGAN, named RAGANBT, which addresses this challenge successfully. Via our extensive experiments, we validate that our RAGANBT is really effective to solve the data sparsity problem, thereby providing existing CF models with great improvement in accuracy under various situations such as basic top-N recommendation, long-tail item recommendation, and recommendation to cold-start users. © 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.title | Rating augmentation with generative adversarial networks towards accurate collaborative filtering | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.identifier.doi | 10.1145/3308558.3313413 | - |
dc.identifier.scopusid | 2-s2.0-85066916904 | - |
dc.identifier.wosid | 000483508402066 | - |
dc.identifier.bibliographicCitation | The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, pp.2616 - 2622 | - |
dc.relation.isPartOf | The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019 | - |
dc.citation.title | The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019 | - |
dc.citation.startPage | 2616 | - |
dc.citation.endPage | 2622 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.subject.keywordPlus | World Wide Web | - |
dc.subject.keywordPlus | Accuracy of classifications | - |
dc.subject.keywordPlus | Adversarial networks | - |
dc.subject.keywordPlus | Data augmentation | - |
dc.subject.keywordPlus | Data sparsity | - |
dc.subject.keywordPlus | Data sparsity problems | - |
dc.subject.keywordPlus | Labeled data | - |
dc.subject.keywordPlus | Recommendation accuracy | - |
dc.subject.keywordPlus | Top-N recommendation | - |
dc.subject.keywordPlus | Collaborative filtering | - |
dc.subject.keywordAuthor | Collaborative filtering | - |
dc.subject.keywordAuthor | Data augmentation | - |
dc.subject.keywordAuthor | Data sparsity | - |
dc.subject.keywordAuthor | Generative adversarial networks | - |
dc.subject.keywordAuthor | Top-N recommendation | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/3308558.3313413 | - |
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