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    Testing the predictive power of variable history web usage

    de Moura Borges, J.L.C and Levene, Mark (2007) Testing the predictive power of variable history web usage. Soft Computing 11 (8), pp. 717-727. ISSN 1432-7643.

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    Abstract

    We present two methods for testing the predictive power of a variable length Markov chain induced from a collection of user web navigation sessions. The collection of sessions is split into a training and a test set. The first method uses a χ2 statistical test to measure the significance of the distance between the distribution of the probabilities assigned to the test trails by a Markov model build from the full collection of sessions and a model built from the training set. The statistical test measures the ability of the model to generalise its predictions to the unseen sessions from the test set. The second method evaluates the model ability to predict the last page of a navigation session based on the preceding pages viewed by recording the mean absolute error of the rank of the last occurring page among the predictions provided by the model. Experimental results conducted on both real and random data sets are reported and the results show that in most cases a second-order model is able to capture sufficient history to predict the next link choice with high accuracy.

    Metadata

    Item Type: Article
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 25 May 2021 18:02
    Last Modified: 25 May 2021 18:02
    URI: https://eprints.bbk.ac.uk/id/eprint/44420

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