title: An effective news recommendation method for microblog user.
abstract: Recommending news stories to users, based on their preferences, has long been a favourite domain for recommender systems research. Traditional systems strive to satisfy their user by tracing users' reading history and choosing the proper candidate news articles to recommend. However, most of news websites hardly require any user to register before reading news. Besides, the latent relations between news and microblog, the popularity of particular news, and the news organization are not addressed or solved efficiently in previous approaches. In order to solve these issues, we propose an effective personalized news recommendation method based on microblog user profile building and sub class popularity prediction, in which we propose a news organization method using hybrid classification and clustering, implement a sub class popularity prediction method, and construct user profile according to our actual situation. We had designed several experiments compared to the state-of-the-art approaches on a real world dataset, and the experimental results demonstrate that our system significantly improves the accuracy and diversity in mass text data.
authors: School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China.
authors: Wanrong
authors: W
authors: Gu
authors: School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China.
authors: Shoubin
authors: S
authors: Dong
authors: School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China.
authors: Zhizhao
authors: Z
authors: Zeng
authors: School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China.
authors: Jinchao
authors: J
authors: He
mesh_terms: D000465:Algorithms; D003146:Communications Media; D006801:Humans; D020407:Internet; D008962:Models, Theoretical