Using Markov Chains for Link Prediction in Adaptive Web Sites

Jianhan Zhu; Jun Hong; John Hughes;

Open Access

doi: 10.1007/3-540-46019-5_5

Published: 01 Jan 2002

Publisher: Springer Berlin Heidelberg

- Summary
- References
  (16)

Abstract

The large number of Web pages on many Web sites has raised\ud navigational problems. Markov chains have recently been used to model user navigational behavior on the World Wide Web (WWW). In this paper, we propose a method for constructing a Markov model of a Web site based on past\ud visitor behavior. We use the Markov model to make link predictions that assist new users to navigate the Web site. An algorithm for transition probability\ud matrix compression has been used to cluster Web pages with similar transition behaviors and compress the transition matrix to an optimal size for efficient probability calculation in link prediction. A maximal forward path method is used to further improve the efficiency of link prediction. Link prediction has been implemented in an online system called ONE (Online Navigation Explorer) to assist users' navigation in the adaptive Web site.

Subjects by Vocabulary

Microsoft Academic Graph classification: Markov model Web page Data mining computer.software_genre computer The Internet business.industry business Link (knot theory) Artificial intelligence Path (graph theory) Markov chain Computer science

arXiv: Computer Science::Information Retrieval

Related Organizations

University of Ulster
United Kingdom

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