Evaluating Variable Length Markov Chain Models for Analysis of User Web Navigation Sessions

TL;DR

This paper reviews and extends methods for variable length Markov chain models to analyze user web navigation, demonstrating that better summarization correlates with higher prediction accuracy.

Contribution

It introduces a novel method to measure how well variable length Markov models summarize user sessions, linking summarization to predictive performance.

Findings

Prediction accuracy increases linearly with summarization ability.

Variable length Markov models effectively capture user navigation patterns.

The proposed methods improve understanding of user behavior on websites.

Abstract

Markov models have been widely used to represent and analyse user web navigation data. In previous work we have proposed a method to dynamically extend the order of a Markov chain model and a complimentary method for assessing the predictive power of such a variable length Markov chain. Herein, we review these two methods and propose a novel method for measuring the ability of a variable length Markov model to summarise user web navigation sessions up to a given length. While the summarisation ability of a model is important to enable the identification of user navigation patterns, the ability to make predictions is important in order to foresee the next link choice of a user after following a given trail so as, for example, to personalise a web site. We present an extensive experimental evaluation providing strong evidence that prediction accuracy increases linearly with summarisation ability.