基于马尔科夫模型和贝叶斯定理的Web用户浏览行为预测模型  

Users ' Web Browsing Behavior Prediction Model Based on Markov Model and Bayesian Theorem

作　　者：毕猛[1,2] 侯林[1] 倪盼[3] 周福才[1] 

机构地区：[1]东北大学软件学院,辽宁沈阳110169 [2]沈阳工业大学管理学院,辽宁沈阳110023 [3]东北大学计算机科学与工程学院,辽宁沈阳110819

出　　处：《东北大学学报（自然科学版）》2016年第6期775-779,799,共6页Journal of Northeastern University(Natural Science)

基　　金：国家科技重大专项(2013ZX03002006);辽宁省科技攻关项目(2013217004);辽宁省博士启动基金资助项目(20141012);沈阳市科技计划项目(F14-231-1-08);中央高校基本科研业务费专项资金资助项目(N130317002)

摘　　要：对用户的Web浏览行为进行分析,既可以使用户减少等待时间,同时也能减轻网络负载.依据Web网站的层次结构特点,首先设计了基于Hash表的反向索引结构来提高数据的预处理速度;在此基础上,利用分层思想构建了基于马尔科夫模型和贝叶斯定理的Web用户浏览行为预测模型.给出了模型的设计思想、相关定义、模型框架以及模型中所涉及的关键构建方法等.最后,对模型进行了实验分析,结果表明在适当的预测准确率前提下,模型能够有效减少在预测时所需的候选网页数量,并大幅提升预测效率.According to the novel aspect of natural hierarchical property of Web site, the inverted index structure was proposed based on Hash table （IIS-HT） to promote the speed of data preprocessing. Based on IIS-HT, a prediction model was also proposed which was based on statistics to predict users＇ browsing behavior. The design idea, definition, framework and key construction methods of the model were also given. Finally, the proposed model was tested with real data. The experimental results show that the model and prediction algorithm could reduce the scope of candidate pages and improve the speed of prediction with adequate accuracy.

关 键 词：WEB站点 用户浏览行为预测 马尔科夫模型 贝叶斯定理 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]