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Predicting Web User’s Behavior: An Absorbing Markov Chain Approach

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 296)

Abstract

We develop a novel predictive modeling framework for Web user behavior with web usage mining (WUM). The proposed predictive model utilizes sequence-based clustering, to group Web users into clusters with similar Web browsing behavior, and absorbing Markov chains (AMC) in order to model Web users’ navigation behavior. Clustering facilitates the prediction of Web users’ navigation behavior by identifying groups of Web users showing similar browsing patterns. The use of AMC allows calculation of transition probabilities and absorbing probabilities at any given time of active user sessions, and thus leads to a better Web personalization and a more effective online advertising outcome. This research will also provide a performance evaluation framework along with the proposed model and suggest a WUM system that can improve ad placement and target marketing in a website.

Keywords

Web mining Predictive analytics Markov chain Clustering 

Notes

Acknowledgment

This research is funded in part by a Belk College Summer Research grant from the Belk College of Business, UNC Charlotte.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.The Belk College of BusinessThe University of North Carolina at CharlotteCharlotteUSA

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