To improve the user s Web page access behavior, improving access efficiency, design a web page recommendation based on Bayesian network model and recommended calculation Method. Through the collection and analysis of server log files described in the file and, using Bayesian network analysis of dependencies between the pages, constructed based on shell Bayesian network model and produce recommended web page set of recommendations. Provided by the Microsoft log data sets on the network to do experiments, you can get over Over 80 accuracy and coverage. Theoretical analysis and experimental results show: online real-time algorithm can make to the user personalized recommendations, with the existing push Recommended algorithm, given recommendation algorithm can quickly set, and can achieve higher accuracy and coverage

一种基于Bayesian 网络的网页推荐算法.pdf

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