TOP-N选择Markov预测模型

分析了访问用户和浏览器的行为,研究了现存的Markov预取模型,并分析了Markov预测模型的本质,在此基础上,提出了基于TOP N选择的Markov预测模型。该模型利用Web访问日志中请求次数大于N的URL生成TOP N,根据用户的访问会话生成Markov链。如果用户当前的访问会话与Markov链匹配,该Markov的下一URL在TOP N中,就把它取到本地缓存。实验表明,该预测模型能有效提高预测精度和命中率,在一定程度上还减少了带宽的需求。

TOP-N selective Markov prediction model

After analyzing the behavior of accessed users, browsers and existing Markov prediction models, a TOP-N selective Markov model was presented to predict user next requests. The TOP-N consisted of URLs which requested time was over N in Web logs. The Markov chains were made up of user visit sessions. If the session which user visited currently matched one of the Markov chains, the next URL of this Markov chains in TOP-N would be prefetched in local cache. The experiment results show that this model can achieve dramatic improvement on predictive accuracy and get a good hit ratio with reducing the traffic load to some extent.