基于Web日志的用户访问推荐系统的研究与实现

The Study and Implementation of Users' Access and Recommendation System Based on Web Log

近年来Internet飞速发展,WWW上的网页也以指数级在增长,面对如此庞大的Web信息,用户很难找到自己所需要的信息。这里给出了一个以日志分析为基础、结合关联规则的用户访问推荐系统(UARS)的框架及其实现,将数据挖掘技术应用于Web日志,通过数据预处理、页面统计分析和用户统计分析,最终通过关联规则挖掘发现用户的频繁访问模式,为用户访问Web站点提供推荐,提高用户访问Web站点的效率。

In recent years internet develops quickly,the Web pages on www also increase exponentially.In the face of such huge information it is difficult for users to find the information they need The framework and implementation of users' access and recommendation system (UARS) is presented here,which is based on Web log analysis and in combination with association rule.UARS applies data mining technology on Web log,it takes a series of steps:data preprocessing,statistical analysis of Web pages and users,finally the discovery of users' frequent access mode by association rule,users' frequent access mode offers recommendation for users' access on the Website,which improves users' efficiency of accessing Website.