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.
| Original language | English |
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| Title of host publication | CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management |
| Publisher | Association for Computing Machinery |
| Pages | 4138-4142 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781450392365 |
| DOIs | |
| Publication status | Published - 2022 Oct 17 |
| Event | 31st ACM International Conference on Information and Knowledge Management, CIKM 2022 - Atlanta, United States Duration: 2022 Oct 17 → 2022 Oct 21 |
Publication series
| Name | International Conference on Information and Knowledge Management, Proceedings |
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Conference
| Conference | 31st ACM International Conference on Information and Knowledge Management, CIKM 2022 |
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| Country/Territory | United States |
| City | Atlanta |
| Period | 22/10/17 → 22/10/21 |
Bibliographical note
Funding Information:This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2022-0-00352) and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.2018R1A5A7059549 and No. 2021R1A2C3004345). Also, we thank the Naver Corporation for their financial support and provisions of the computing facilities, which helped us greatly in performing this research successfully.
Publisher Copyright:
© 2022 ACM.
All Science Journal Classification (ASJC) codes
- Business, Management and Accounting(all)
- Decision Sciences(all)