关键词: 数据挖掘, 个性化推荐, 协同过滤, 贝叶斯网络

Abstract:

A model based on Bayesian network and corresponding algorithm for web page recommendation were presented to improve users’ behavior on browsing web pages and enhance visiting efficiency. The model was constructed by collecting and analyzing the description files and log files in the servers and using the Bayesian network to analyze the dependence among the web pages. Then the model was built and the recommendation set was generated. By conducting experiments on the network log data sets provided by Microsoft Company, the obtainea precision and coverage were both higher than 80％. The results of theoretical analysis and experiments indicated that the algorithm could make personalized recommendation for users in real time online. Compared with other existing algorithms, this algorithm could give the recommendation set more quickly with higher precision and coverage.

Key words:  Data mining, personalized recommendation, collaborative filtering, Bayesian network