Advertisement

A user model neural network for a personal news service

  • Andrew Jennings
  • Hideyuki Higuchi
Article

Abstract

User modelling has been widely applied to pedantic situations, where we are attempting to infer the user's knowledge. In teaching it is important to know that the user has mastered the elementary concepts before proceeding with the advanced topics. However, the application of user modelling to information retrieval demands a quite different type of user model. Here we construct a user model for browsing, where the user is uncertain of exactly which information he desires. This requires a more inexact and robust user model, that can quickly give guidance to the system. We propose a user model based on neural networks that can be constructed incrementally. Performance of the model shows some promise for this approach. We discuss the advantages and limitations of the approach and its implications for user modelling.

Key words

Neural networks information retrieval browsing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Allen, R. B.: 1990, ‘User Models: Theory, Method, and Practice’.Int. J. Man-Machine Studies,32, 511–543.Google Scholar
  2. Baclaski, P. W.: 1991, ‘Personal Information Intake Filtering’. In:Proceedings of the Bellcore Information Filtering Workshop, pp. 1–14.Google Scholar
  3. Blair, D. C.: 1986, ‘Indeterminacy in the Subject Access to Documents’.Information Processing and Management,22, 229–241.Google Scholar
  4. Blair D. C. and M. E. Moran: 1985, ‘An Evaluation of Retrieval Effectiveness for a Full-text Document Retrieval System’.Communications of the ACM,28(3), 289–299.Google Scholar
  5. Brajnik G., G. Guida and C. Tasso: 1987, ‘User Modelling in Intelligent Information Retrieval’.Information Processing and Management,23(4), 305–320.Google Scholar
  6. Brajnik G., C. Tasso and A. Vaccher: 1991, ‘A Shell for Non-monotonic User Modelling Systems’. In:Proceedings of the IJCA1 Workshop on Agent Modelling for Intelligent Interaction, Sydney.Google Scholar
  7. Chen H. and V. Dhar: 1990, ‘User Misconceptions of Information Retrieval Systems’.Int. J. Man-Machine Studies,32, 673–692.Google Scholar
  8. Daniels P. J.: 1986, ‘Cognitive Models in Information Retrieval — An Evaluative Review’.Journal of Documentation,42(4), 272–304.Google Scholar
  9. Deerwester S., S. T. Dumais, G. W. Furnas, T. K. Landauer and R. Harshman: 1990, ‘Indexing by Latent Semantic Analysis’.Journal of the American Society for Information Science,41(6), 391–407.Google Scholar
  10. Eirund H.: 1989, ‘Knowledge-based Document Classification Supporting Content Based Retrieval and Mail Distribution’. In: K. Boyanov and R. Angelinov (eds.):Network Information Processing Systems, Elsevier Science Publishers, pp. 309–320.Google Scholar
  11. Foltz P. W.: 1990, ‘Using Latent Semantic Indexing for Information Filtering’. In:Proceedings of the Fifth Conference on Office Information Systems, pp. 40–47.Google Scholar
  12. Fox E. A.: 1988, ‘Development of the Coder System: A Testbed for Artificial Intelligence Methods in Information Retrieval’.Information Processing and Management,23(4), 341–366.Google Scholar
  13. Frei H. P. and P. Schauble: 1991, ‘Determining the Effectiveness of Retrieval Algorithms’.Information Processing and Management,27(2/3), 153–164.Google Scholar
  14. Furnas G. W., T. K. Landauer, L. M. Gomez and S. T. Dumais: 1987, ‘The Vocabulary Problem in Human-system Communication’.Communications of the ACM,30(11), 964–971.Google Scholar
  15. Gomez L. M. and C. C. Lochbaum: 1990, ‘All the Right Words: Finding What you Want as a Function of Richness of Indexing Vocabulary’.Journal of the American Society for Information Science,41(8), 547–559.Google Scholar
  16. Gordon M.: 1988, ‘Probabilistic and Genetic Algorithms for Document Retrieval’.Communications of the ACM,31(10), 1208–1218.Google Scholar
  17. Gordon M. and M. Kochen: 1989, ‘Recall-precision Trade-off: A Derivation’.Journal of the American Society for Information Science,40(3), 145–151.Google Scholar
  18. Harman D.: 1986, ‘An Experimental Study of Factors Important in Document Ranking’. In:Proc. ACM Conference on R&D in Information Retrieval, Pisa, Italy.Google Scholar
  19. Howells T.: 1988, ‘Vital: A Connectionist Parser’. In:Proceedings of the Tenth Annual Conference of the Cognitive Science Society, Montreal, Canada.Google Scholar
  20. Jones W. and G. W. Furnas: 1987, ‘Pictures of Relevance: A Geometric Analysis of Similarity Measures’.Journal of the American Society for Information Science,38(6), 420–442.Google Scholar
  21. Jones W. P.: 1988, ‘As we May Think: Psychological Considerations in the Design of a Personal Filing System’. In:Cognitive Science and its Applications for Human-Computer Interaction. New Jersey: Lawrence Erlbaum.Google Scholar
  22. Kanou Y., Matsumoto, T. and Nakamura, Y.: 1990, ‘A User Model Representing Multiple Requests for an Intelligent Document Retrieval System’. SIG-HICG-8902-110/4, Japanese Information Processing Society.Google Scholar
  23. Mozer M.: 1984, ‘Inductive Information Retrieval Using Parallel Distributed Computation’, ICS, UCSD.Google Scholar
  24. Oddy R. N.: 1977, ‘Information Retrieval Through Man-machine Dialogue’.Journal of Documentation,33(1), 1–14.Google Scholar
  25. Orwant J.: 1991, ‘The Doppelganger User Modelling System’.Proceedings of the IJCAI Workshop on Agent Modelling Sydney, Australia, pp. 164–168.Google Scholar
  26. Raghavan V. V. and J. S. Deogun: 1987, ‘Optimal Determination of User-oriented Clusters’. In:Proc. 10th Annual Conference on R&D in Information Retrieval, pp. 140–146, New Orleans, USA.Google Scholar
  27. Rich E.: 1983, ‘Users are Individuals: Individualizing User Models’.International Journal of Man-Machine Studies,18, 199–214.Google Scholar
  28. Salton G.: 1986, ‘Another Look at Automatic Text-retrieval Systems’.Communications of the ACM,29(7), 648–656.Google Scholar
  29. Salton G. and C. Buckley: 1991, ‘Global Text Matching for Information Retrieval’.Science,253(5023), 1012–1015.Google Scholar
  30. Sparck Jones K.: 1972, ‘A Statistical Interpretation of Term Specificity and its Applications in Retrieval’.Journal of Documentation,28, 11–21.Google Scholar
  31. Thompson R. H. and W. B. Croft: 1989, ‘Support for Browsing in an Intelligent Text Retrieval System’.International Journal of Man-Machine Studies,30, 639–668.Google Scholar
  32. Van Rijsbergen C. J.: 1977, ‘A Theoretical Basis for the Use of Co-occurrence Data in Information Retrieval’.Journal of Documentation,33(2), 106–119.Google Scholar
  33. Vigil P.J.: 1983, ‘The Psychology of Online Searching’.Journal of the American Society for Information Science,34(4), 281–287.Google Scholar
  34. Wilkinson R. and P. Kingston: 1991, ‘Using the Cosine Measure in a Neural Network for Information Retrieval’. In:Proceedings of the 14th Conference on R&D in Information Retrieval, Chicago, USA.Google Scholar

Copyright information

© Kluwer Academic Publishers 1993

Authors and Affiliations

  • Andrew Jennings
    • 1
  • Hideyuki Higuchi
    • 2
  1. 1.Telecom Australia Research LaboratoriesClaytonAustralia
  2. 2.Kansai Advanced Research CenterHyogoJapan

Personalised recommendations