基于聚类的智能网页推荐系统研究

设计了一种智能网页推荐系统的架构,其中包括数据预处理、聚类分析和网页推荐3个子系统,可以根据网站的访问日志来对用户进行自动分类,进而对网站的新用户在线提供网页推荐。提出了路径间距离的计算方法,进而研究了聚类子系统的结构,并通过对微软网站中用户访问日志的仿真实验,说明了所述方法的有效性。

Research of Intelligent Web Page Recommender System Based on Clustering Technique

With the size of WWW growing bigger, its structure is more and more complex. Web visitors with personalized information demands find it very difficult to locate information with ease. In this paper, we designed an intelligent web page recommender system, which include three sub-systems of data preprocessing, clustering, and web page recommendation. The proposed system can divide visitors into subgroups automatically, thus providing new users with online web page recommendation. The formula for computing the distance between visitor paths is proposed, followed by the study of the structure of clustering system. The simulation results based on web log of Microsoft website proved the validity of the proposed method.