Web page prediction using Markov model and Bayesian statistics

Owing to the popularity of World Wide Web, many organizations have adapted different strategies to do their business, which enhance the rapid development of E-commerce directly and make 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 result of the prediction can be used in different areas like 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 statistics 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 implementation will show the efficiency of the provided model.