Selective Markov models for predicting Web page accesses

Mukund Deshpande, George Karypis

Research output: Contribution to journalArticlepeer-review

273 Scopus citations


The problem of predicting a user's behavior on a Web site has gained importance due to the rapid growth of the World Wide Web and the need to personalize and influence a user's browsing experience. Markov models and their variations have been found to be well suited for addressing this problem. Of the different variations of Markov models, it is generally found that higher-order Markov models display high predictive accuracies on Web sessions that they can predict. However, higher-order models are also extremely complex due to their large number of states, which increases their space and run-time requirements. In this article, we present different techniques for intelligently selecting parts of different order Markov models so that the resulting model has a reduced state complexity, while maintaining a high predictive accuracy.

Original languageEnglish (US)
Pages (from-to)163-184
Number of pages22
JournalACM Transactions on Internet Technology
Issue number2
StatePublished - May 2004


  • Markov models
  • Predicting user behavior
  • Web mining
  • World wide web


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