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

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Book cover Internetworked World (WEB 2016)

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

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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.

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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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Authors and Affiliations

  1. The Belk College of Business, The University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC, 28223, USA

    Sungjune Park & Vinay Vasudev

Authors
  1. Sungjune Park
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  2. Vinay Vasudev
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Corresponding author

Correspondence to Sungjune Park .

Editor information

Editors and Affiliations

  1. University of Washington, Seattle, Washington, USA

    Ming Fan

  2. University of Turku, Turku, Finland

    Jukka Heikkilä

  3. University of Turku, Turku, Finland

    Hongxiu Li

  4. University of Illinois, Urbana-Champaign, Illinois, USA

    Michael J. Shaw

  5. Georgia Institute of Technology, Atlanta, Georgia, USA

    Han Zhang

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Park, S., Vasudev, V. (2017). Predicting Web User’s Behavior: An Absorbing Markov Chain Approach. In: Fan, M., Heikkilä, J., Li, H., Shaw, M., Zhang, H. (eds) Internetworked World. WEB 2016. Lecture Notes in Business Information Processing, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-319-69644-7_17

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  • DOI: https://doi.org/10.1007/978-3-319-69644-7_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69643-0

  • Online ISBN: 978-3-319-69644-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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