Selective Markov Models for Predicting Web-Page Accesses

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. Keywords: world wide web, web mining, Markov models, predicting user behavior.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Oct 30, 2000
Accession Number
ADA439247

Entities

People

  • George Karypis
  • Mukund Deshpande

Organizations

  • University of Minnesota

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Computer Communications
  • Computer Science
  • Data Sets
  • Electronic Commerce
  • High Performance Computing
  • Information Science
  • Markov Models
  • Models
  • Network Science
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Test Sets
  • Websites
  • Word Processors
  • World Wide Web

Fields of Study

  • Computer science
  • Engineering

Readers

  • Computational Modeling and Simulation
  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Statistical inference.

Technology Areas

  • Space