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A Comparative Study for State-of-the-Art News Recommendation Methods
- Bae, Hong-Kyun;
- Ahn, Jeewon;
- Kim, Sang-Wook
초록
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.
키워드
deep-learning model; feature extraction; hybrid recommendation; news recommender system
- 제목
- A Comparative Study for State-of-the-Art News Recommendation Methods
- 저자
- Bae, Hong-Kyun; Ahn, Jeewon; Kim, Sang-Wook
- 발행일
- 2022-10
- 유형
- Proceeding
- 저널명
- International Conference on Next Generation Computing
- 페이지
- 140 ~ 142