A novel prediction model based on hierarchical characteristic of web site
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摘要
Internet has developed in a rapid way in the recent 10 years,and the information of web site has also been increasing fast. Predicting web user’s behavior becomes a crucial issue following the purposes like increasing the user’s browsing speed efficiently, decreasing the user’s latency as well as possible and reducing the loading of web server. In this paper, we propose an efficient prediction model, two-level prediction model (TLPM), using a novel aspect of natural hierarchical property from web log data. TLPM can decrease the size of candidate set of web pages and increase the speed of predicting with adequate accuracy. The experiment results prove that TLPM can highly enhance the performance of prediction when the number of web pages is increasing.
论文关键词:Web usage mining,Prediction,Data preprocessing,Markov model,Bayesian theorem
论文评审过程:Available online 15 September 2010.
论文官网地址:https://doi.org/10.1016/j.eswa.2010.08.128