Is It Enough Just Looking at the Title?: Leveraging Body Text To Enrich Title Words Towards Accurate News Recommendation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Taeho | - |
dc.contributor.author | Kim, Yungi | - |
dc.contributor.author | Lee, Yeon-Chang | - |
dc.contributor.author | Shin, Won-Yong | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.date.accessioned | 2023-08-01T06:55:42Z | - |
dc.date.available | 2023-08-01T06:55:42Z | - |
dc.date.created | 2023-07-21 | - |
dc.date.issued | 2022-10 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188587 | - |
dc.description.abstract | In a news recommender system, a user tends to click on a news article if she is interested in its topic understood by looking at its title. Such a behavior is possible since, when viewing the title, humans naturally think of the contextual meaning of each title word by leveraging their own background knowledge. Motivated by this, we propose a novel personalized news recommendation framework CAST (Context-aware Attention network with a Selection module for Title word representation), which is capable of enriching title words by leveraging body text that fully provides the whole content of a given article as the context. Through extensive experiments, we demonstrate (1) the effectiveness of core modules in CAST, (2) the superiority of CAST over 9 state-of-the-art news recommendation methods, and (3) the interpretability with CAST. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ACM CIKM 2022 | - |
dc.title | Is It Enough Just Looking at the Title?: Leveraging Body Text To Enrich Title Words Towards Accurate News Recommendation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.identifier.doi | 10.1145/3511808.3557619 | - |
dc.identifier.scopusid | 2-s2.0-85140844208 | - |
dc.identifier.wosid | 001074639604034 | - |
dc.identifier.bibliographicCitation | ACM Conference on Information and Knowledge Management, pp.4138 - 4142 | - |
dc.relation.isPartOf | ACM Conference on Information and Knowledge Management | - |
dc.citation.title | ACM Conference on Information and Knowledge Management | - |
dc.citation.startPage | 4138 | - |
dc.citation.endPage | 4142 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | other | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.subject.keywordPlus | Background knowledge | - |
dc.subject.keywordPlus | Context-Aware | - |
dc.subject.keywordPlus | Context-aware attention network | - |
dc.subject.keywordPlus | News articles | - |
dc.subject.keywordPlus | News recommendation | - |
dc.subject.keywordPlus | News recommender systems | - |
dc.subject.keywordPlus | Personalized news | - |
dc.subject.keywordPlus | Recommendation methods | - |
dc.subject.keywordPlus | State of the art | - |
dc.subject.keywordPlus | Word representations | - |
dc.subject.keywordAuthor | context-aware attention network | - |
dc.subject.keywordAuthor | news recommendation | - |
dc.identifier.url | https://dl.acm.org/doi/abs/10.1145/3511808.3557619 | - |
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