News Contents Recommendation Model Based on Feedback of Web Usage

Author

Ping Ni ; Liao, Jianxin ; Zhu, Xiaomin ; Ren, Keyan

Author_Institution

State Key Lab. of Networking & Switching Technol., Beijing Univ. of Posts & Telecommun., Beijing, China

Volume

4

fYear

2009

fDate

March 31 2009-April 2 2009

Firstpage

431

Lastpage

435

Abstract

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.

Keywords

Web sites; classification; data mining; document handling; information retrieval; K-means classification; ReK-means method; Web document categorization; Web site usage mining feedback; keyword extraction; multivocal word; news content reclassification; news content recommendation model; related words classification algorithm; Automation; Clustering algorithms; Computer science; Data mining; Electronics industry; Information technology; Laboratories; State feedback; Telecommunication switching; Web server; machine learning; news recommendation; web mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Information Engineering, 2009 WRI World Congress on

Conference_Location

Los Angeles, CA

Print_ISBN

978-0-7695-3507-4

Type

conf

DOI

10.1109/CSIE.2009.104

Filename

5171033