Web User Behavior Analysis Using Improved Naïve Bayes Prediction Algorithm

With the continued growth and proliferation of Web services and Web based information systems, the volumes of user data have reached astronomical proportions. Analyzing such data using Web Usage Mining can help to determine the visiting interests or needs of the web user. As web log is incremental in nature, it becomes a crucial issue to predict exactly the ways how users browse websites. It is necessary for web miners to use predictive mining techniques to filter the unwanted categories for reducing the operational scope. Markov models& its variations have also been used to analyze web navigation behavior of users. A user's web link transition on a particular website can be modeled using first, second-order or higher-order Markov models and can be used to make predictions regarding future navigation and to personalize the web page for an individual user. All higher order Markov model holds the promise of achieving higher prediction accuracies, improved coverage than any single-order Markov model but holds high state space complexity. Hence a Hybrid Markov Model is required to improve the operation performance and prediction accuracy significantly. 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.