원문정보
한국차세대컴퓨팅학회
한국차세대컴퓨팅학회 학술대회
The 8th International Conference on Next Generation Computing 2022
2022.10
pp.140-142
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
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.
목차
Abstract
I. INTRODUCTION
II. NEWS RECOMMENATION METHODS
III. EMPIRICAL EVALUATION
A. Experimental Setup
B. Experimental Result
IV. CONCLUSION
REFERENCES
I. INTRODUCTION
II. NEWS RECOMMENATION METHODS
III. EMPIRICAL EVALUATION
A. Experimental Setup
B. Experimental Result
IV. CONCLUSION
REFERENCES
저자정보
참고문헌
자료제공 : 네이버학술정보