The Navigation Problem in the World-Wide-Web

Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Herein we build statistical foundations for tackling the navigation problem users encounter during web interaction, based on a formal model of the web in terms of a probabilistic automaton, which can also be viewed as a finite ergodic Markov chain. In our model of the web the probabilities attached to state transitions have two interpretations, namely, they can denote the proportion of times a user followed a link, and alternatively they can denote the expected utility of following a link. Using this approach we have developed two techniques for constructing a web view based on the two interpretations of the probabilities of links, where a web view is a collection of relevant trails. The first method we describe is concerned with finding frequent user behaviour patterns. A collection of trails is taken as input and an ergodic Markov chain is produced as output with the probabilities of transitions corresponding to the frequency the user traversed the associated links. The second method we describe is a reinforcement learning algorithm that attaches higher probabilities to links whose expected trail relevance is higher. The user’s home page and a query are taken as input and an ergodic Markov chain is produced as output with the probabilities of transitions giving the expected utility of following their associated links. Finally, we characterise typical user navigation sessions in terms of the entropy of the underlying ergodic Markov chain.


Linear Temporal Logic Anchor Node Reinforcement Learning Algorithm Navigation Problem Link Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. J. Borges and M. Levene. Data mining of user navigation patterns. In Proceedings of Workshop on Web Usage Analysis and User Profiling (WEBKDD), in conjunction with ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 31–36, San Diego, Ca., 1999. Long version submitted for publication.Google Scholar
  2. R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. ACM Press and Addison-Wesley, Reading, Ma., 1999.Google Scholar
  3. T.M. Cover and J.A. Thomas. Elements of Information Theory. Wiley Series in Telecommunications. John Wiley & Sons, Chichester, 1991.zbMATHCrossRefGoogle Scholar
  4. E.A. Emerson. Temporal and modal logic. In: J. Van Leeuwen, ed.: Handbook of Theoretical Computer Science, volume B, chapter 16, pages 997–1072. Elsevier Science Publishers, Amsterdam, 1990.Google Scholar
  5. J.E. Hopcroft and J.D. Ullman. Introduction to Automata Theory, Languages and Computation. Addison-Wesley, Reading, Ma., 1979.zbMATHGoogle Scholar
  6. J.G. Kemeny and J.L. Snell. Finite Markov Chains. D. Van Nostrand, Princeton, NJ, 1960.zbMATHGoogle Scholar
  7. S. Lawrence and C.L. Giles. Accessibility of information on the web. Nature, 400:107–109, 1999.CrossRefGoogle Scholar
  8. M. Levene and G. Loizou. Computing the entropy of user navigation in the web. Research Note RN/99/42, Department of Computer Science, University College London, 1999.Google Scholar
  9. M. Levene and G. Loizou. Navigation in hypertext is easy only sometimes. SIAM Journal on Computing, 29:728–760, 1999.MathSciNetCrossRefGoogle Scholar
  10. M. Levene and G. Loizou. A probabilistic approach to navigation in hypertext. Information Sciences, 114:165–186, 1999.MathSciNetzbMATHCrossRefGoogle Scholar
  11. J. Nielsen. Hypertext and Hypermedia. Academic Press, Boston, Ma., 1990.Google Scholar
  12. N. Zin and M. Levene. Constructing web views from automated navigation sessions. In Proceedings of ACM Digital Library Workshop on Organizing Web Space (WOWS), pages 54–58, Berkeley, Ca., 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity College LondonLondonUK

Personalised recommendations