Web navigation prediction using multiple evidence combination and domain knowledge

Mamoun A. Awad, Latifur R. Khan

    Research output: Contribution to journalArticlepeer-review

    33 Citations (Scopus)

    Abstract

    Predicting users' future requests in the World Wide Web can be applied effectively in many important applications, such as web search, latency reduction, and personalization systems. Such application has traditional tradeoffs between modeling complexity and prediction accuracy. In this paper, we study several hybrid models that combine different classification techniques, namely, Markov models, artificial neural networks (ANNs), and the All- Kth-Markov model, to resolve prediction using Dempster's rule. Such fusion overcomes the inability of the Markov model in predicting beyond the training data, as well as boosts the accuracy of ANN, particularly, when dealing with a large number of classes. We also employ a reduction technique, which uses domain knowledge, to reduce the number of classifiers to improve the predictive accuracy and the prediction time of ANNs. We demonstrate the effectiveness of our hybrid models by comparing our results with widely used techniques, namely, the Markov model, the All-Kth-Markov model, and association rule mining, based on a benchmark data set.

    Original languageEnglish
    Pages (from-to)1054-1062
    Number of pages9
    JournalIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
    Volume37
    Issue number6
    DOIs
    Publication statusPublished - Nov 2007

    Keywords

    • Artificial neural networks (ANNs)
    • Association rule mining (ARM)
    • Dempster's rule
    • Markov model
    • Markov processes
    • N-gram
    • Neural nets
    • Prediction theory
    • Search engines

    ASJC Scopus subject areas

    • Software
    • Control and Systems Engineering
    • Human-Computer Interaction
    • Computer Science Applications
    • Electrical and Electronic Engineering

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