Web Access Prediction Model Based on Markov Chain and Association Rule

用户访问预测是根据用户的历史访问信息和当前的访问路径预测用户下一步或将来可能访问的页面.因此可以利用预测结果提高服务器的性能,提高缓存的利用率和为用户提供个性化服务.提出了基于MArkOV链和关联规则的预测模型MAPM(MArkOV CHAIn And ASSOCIATIOn rulE PrEdICTIOn MOdEl),该模型首先使用二阶MArkOV链找到用户下一步或将来可能访问的页面集,生成预测候选集;然后再使用二项关联规则从正向和反向两个角度修正MArkOV的预测结果,从而生成最后的预测页面.User access prediction is the core of Web log mining,which predicts the next access page or the future access pages according to the history access information and the current access path.We can make use of the prediction result to improve the Web server performance,increase the cache utilization and provide users with personal service.In this paper we proposed Markov chain and association rule prediction model(MAPM).This model uses second-order Markov chain to find the pages which users may visit in next step or future,so as to generate the candidate prediction page set,and then corrects the Markov prediction result on forward and reverse perspective according to the two-items association rules,and gets the last prediction page.国家自然科学基金(40627001