Is It Enough Just Looking at the Title?: Leveraging Body Text To Enrich Title Words Towards Accurate News Recommendation
- Authors
- Kim, Taeho; Kim, Yungi; Lee, Yeon-Chang; Shin, Won-Yong; Kim, Sang-Wook
- Issue Date
- Oct-2022
- Publisher
- ACM CIKM 2022
- Keywords
- context-aware attention network; news recommendation
- Citation
- ACM Conference on Information and Knowledge Management, pp.4138 - 4142
- Indexed
- OTHER
- Journal Title
- ACM Conference on Information and Knowledge Management
- Start Page
- 4138
- End Page
- 4142
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188587
- DOI
- 10.1145/3511808.3557619
- 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.
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