Los Angeles, CA
March 31, 2009 to April 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.104
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.
news recommendation, web mining, machine learning
Ping Ni, Jianxin Liao, Xiaomin Zhu, Keyan Ren, "News Contents Recommendation Model Based on Feedback of Web Usage", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 431-435, doi:10.1109/CSIE.2009.104