Predicting WWW surfing using multiple evidence combination

Mamoun Awad, Latifur Khan, Bhavani Thuraisingham

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)


The improvement of many applications such as web search, latency reduction, and personalization/ recommendation systems depends on surfing prediction. Predicting user surfing paths involves tradeoffs between model complexity and predictive accuracy. In this paper, we combine two classification techniques, namely, the Markov model and Support Vector Machines (SVM), to resolve prediction using Dempster's rule. Such fusion overcomes the inability of the Markov model in predicting the unseen data as well as overcoming the problem of multiclassification in the case of SVM, especially when dealing with large number of classes. We apply feature extraction to increase the power of discrimination of SVM. In addition, during prediction we employ domain knowledge to reduce the number of classifiers for the improvement of accuracy and the reduction of prediction time. We demonstrate the effectiveness of our hybrid approach by comparing our results with widely used techniques, namely, SVM, the Markov model, and association rule mining.

Original languageEnglish
Pages (from-to)401-417
Number of pages17
JournalVLDB Journal
Issue number3
Publication statusPublished - May 2008
Externally publishedYes

ASJC Scopus subject areas

  • Information Systems
  • Hardware and Architecture


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