This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
2009 WRI World Congress on Computer Science and Information Engineering
News Contents Recommendation Model Based on Feedback of Web Usage
Los Angeles, California USA
March 31-April 02
ISBN: 978-0-7695-3507-4
In this paper, reclassification for the current classification through K-means would be implemented based on the feedback of web usage mining in order to improve the accuracy of news recommendation and convergence of classification. It could extract most relative keywords and eliminate the disturbance of multi-vocal word in one category based on feedback of web usage. The reclassification of news contents would be implemented based on K-Means algorithm and web usage mining result. We call this method as ReK-means. By simulation comparing, accuracy of reclassification were obvious to be improved compared with related words classification algorithm.
Index Terms:
news recommendation, web mining, machine learning
Citation:
Ping Ni, Jianxin Liao, Xiaomin Zhu, Keyan Ren, "News Contents Recommendation Model Based on Feedback of Web Usage," csie, vol. 4, pp.431-435, 2009 WRI World Congress on Computer Science and Information Engineering, 2009
Usage of this product signifies your acceptance of the Terms of Use.