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The Scientific World Journal
Volume 2014, Article ID 734351, 8 pages
http://dx.doi.org/10.1155/2014/734351
Research Article

Hot News Recommendation System from Heterogeneous Websites Based on Bayesian Model

Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China

Received 16 March 2014; Accepted 16 June 2014; Published 26 June 2014

Academic Editor: Jorge Garcia Duque

Copyright © 2014 Zhengyou Xia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The most current news recommendations are suitable for news which comes from a single news website, not for news from different heterogeneous news websites. Previous researches about news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing hundreds of heterogeneous news websites to provide top hot news services for group customers (e.g., government staffs). In this paper, we propose a hot news recommendation model based on Bayesian model, which is from hundreds of different news websites. In the model, we determine whether the news is hot news by calculating the joint probability of the news. We evaluate and compare our proposed recommendation model with the results of human experts on the real data sets. Experimental results demonstrate the reliability and effectiveness of our method. We also implement this model in hot news recommendation system of Hangzhou city government in year 2013, which achieves very good results.