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A Comparative Study for State-of-the-Art News Recommendation Methods
- Authors
- Bae, Hong-Kyun; Ahn, Jeewon; Kim, Sang-Wook
- Issue Date
- Oct-2022
- Publisher
- 한국차세대컴퓨팅학회
- Keywords
- deep-learning model; feature extraction; hybrid recommendation; news recommender system
- Citation
- International Conference on Next Generation Computing, pp 140 - 142
- Pages
- 3
- Indexed
- OTHER
- Journal Title
- International Conference on Next Generation Computing
- Start Page
- 140
- End Page
- 142
- 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.
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Related Researcher
- Kim, Sang-Wook
- COLLEGE OF ENGINEERING (SCHOOL OF COMPUTER SCIENCE)