An Efficient Hybrid Successive Markov Model for Predicting Web User Usage Behavior using Web Usage Mining

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. The first-order Markov model has low accuracy in achieving right predictions, which is why extensions to higher order models are necessary. 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. The present paper introduces An Efficient Hybrid Successive Markov Prediction Model, HSMP. The HSMP model is initially predicts the possible wanted categories using Relevance factor, which can be used to infer the users’ browsing behavior between web categories. Then predict the pages in predicted categories using techniques for intelligently combining different order Markov models so that the resulting model has low state complexity, improved prediction accuracy and retains the coverage of the all higher order Markov model. These techniques eliminates low support states, evaluates the probability distribution and estimates the error associated with each state without affecting the overall accuracy as well as protection of the resulting model. To validate the proposed prediction model, several experiments were conducted and results proven this are claimed in this paper.