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A Comparative Study for State-of-the-Art News Recommendation Methods
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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Bae, Hong-Kyun | - |
| dc.contributor.author | Ahn, Jeewon | - |
| dc.contributor.author | Kim, Sang-Wook | - |
| dc.date.accessioned | 2023-08-07T07:42:05Z | - |
| dc.date.available | 2023-08-07T07:42:05Z | - |
| dc.date.issued | 2022-10 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188875 | - |
| dc.description.abstract | As a massive number of real-time news makes it difficult for users to find their preferred news, various news recommender systems have been actively proposed in the research field. With the two popular real-world datasets in a news domain, Adressa and MIND, we compare the four state-of-the-art news recommendation methods (i.e., NRMS, LSTUR, NAML, and CNE-SUE) in terms of accuracy. Also, we investigate the strengths and weaknesses of news recommendation methods depending on datasets or metrics. | - |
| dc.format.extent | 3 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | 한국차세대컴퓨팅학회 | - |
| dc.title | A Comparative Study for State-of-the-Art News Recommendation Methods | - |
| dc.type | Article | - |
| dc.publisher.location | 대한민국 | - |
| dc.identifier.bibliographicCitation | International Conference on Next Generation Computing, pp 140 - 142 | - |
| dc.citation.title | International Conference on Next Generation Computing | - |
| dc.citation.startPage | 140 | - |
| dc.citation.endPage | 142 | - |
| dc.type.docType | Proceeding | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | other | - |
| dc.subject.keywordAuthor | deep-learning model | - |
| dc.subject.keywordAuthor | feature extraction | - |
| dc.subject.keywordAuthor | hybrid recommendation | - |
| dc.subject.keywordAuthor | news recommender system | - |
| dc.identifier.url | http://www.icngc.org/bbs/content.php?co_id=program | - |
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Related Researcher
- Kim, Sang-Wook
- COLLEGE OF ENGINEERING (SCHOOL OF COMPUTER SCIENCE)