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

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.
Abstract：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.