Learning and Predicting Key Web Navigation Patterns Using Bayesian Models

Abstract

The accurate prediction of Web navigation patterns has immense commercial value as the Web evolves into a primary medium for marketing and sales for many businesses. Often these predictions are based on complex temporal models of users’ behavior learned from historical data. Such an approach, however, is not readily understandable by business people and hence less likely to be used. In this paper, we consider several key and practical Web navigation patterns and present Bayesian models for their learning and prediction. The navigation patterns considered include pages (or page categories) visited in first N positions, type of visit (short or long), and rank of page categories visited in first N positions. The patterns are learned and predicted for specific users, time slots, and user-time slot combinations. We employ Bayes rule and Markov chain in our learning and prediction models. The focus is on accuracy and simplicity rather than modeling the complex Web user behavior. We evaluate our models on four weeks of Web navigation data. Prediction models are learned from the first three weeks of data and the predictions are tested on last week’s data. The results confirm the high accuracy and good efficiency of our models.