A COMPARISON OF SCORING METRICS FOR PREDICTING THE NEXT NAVIGATION STEP WITH MARKOV MODEL-BASED SYSTEMS

José Borges () and Mark Levene ()
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José Borges: School of Engineering, University of Porto, R. Dr. Roberto Frias, 4200 — Porto, Portugal
Mark Levene: School of Computer Science and Information Systems, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK

International Journal of Information Technology & Decision Making (IJITDM), 2010, vol. 09, issue 04, 547-573

Abstract: The problem of predicting the next request during a user's navigation session has been extensively studied. In this context, higher-order Markov models have been widely used to model navigation sessions and to predict the next navigation step, while prediction accuracy has been mainly evaluated with the hit and miss score. We claim that this score, although useful, is not sufficient for evaluating next link prediction models with the aim of finding a sufficient order of the model, the size of a recommendation set, and assessing the impact of unexpected events on the prediction accuracy. Herein, we make use of a variable length Markov model to compare the usefulness of three alternatives to the hit and miss score: the Mean Absolute Error, the Ignorance Score, and the Brier score. We present an extensive evaluation of the methods on real data sets and a comprehensive comparison of the scoring methods.

Keywords: Web usage mining; variable length Markov model; sequential prediction; scoring metrics (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1142/S0219622010003956

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