Abstract
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 well suited for addressing this problem. Of the different variations or Markov models it is generally found that higher-order Markov models display high predictive accuracies. However higher order models are also extremely complicated due to their large number of states that increases their space and runtime requirements. In this paper we present different techniques for intelligently selecting parts of different order Markov models so that the resulting model has a reduced state complexity and improved prediction accuracy. We have tested our models on various datasets and have found that their performance is consistently superior to that obtained by higher-order Markov models.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
