An algorithm based on the Bayesian network for web page recommendation
Guilin Chen · Journal of Shandong University · 2011
A model based on the 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 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 precision and coverage obtained 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.