Two Levels of Prediction Model for User ’ s Browsing Behavior 1

Owing to the popularity of World Wide Web, many enterprises have changed the ways of doing business, which enhance the rapid development of E-commerce directly and makes the development of web usage mining skills important. It becomes a crucial issue to predict exactly the ways how users and customers browse websites. The prediction result can be used for personalization, building proper websites, promotion, getting marketing information, and forecasting market trends etc. Markov model is assumed to be a probability model by which users’ browsing behaviors can be predicted at category level. Bayesian theorem can also be applied to present and infer users’ browsing behaviors at webpage level. In this research, Markov models and Bayesian theorem are combined and a two-level prediction model is designed. By the Markov Model, the system can effectively filter the possible category of the websites and Bayesian theorem will help to predict websites accuracy. The experiments will show that our provided model has noble hit ratio for prediction.