Skip to main content

A Personalized Information Search Process Based on Dialoguing Agents and User Profiling

  • Conference paper
  • First Online:
Advances in Information Retrieval (ECIR 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2633))

Included in the following conference series:

Abstract

The amount of information available on the web, as well as the number of e-businesses and web shoppers, is growing exponentially. Customers have to spend a lot of time to browse the net in order to find relevant information. One way to overcome this problem is to use dialoguing agents that exploit user profiles to generate personal recommendations. This paper presents a system, designed according to this approach, that adopts a query refinement mechanism to improve the search process of an Internet commerce web site.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. L’Abbate, M., Thiel, U.: Chatterbots and Intelligent Information Search. In: Proceedings of the BCS-IRSG 23rd European Colloquium on Information Retrieval Research (2001) 200–207

    Google Scholar 

  2. Adomavicius, G., Tuzhilin, A.: Using Data Mining Methods to Build Customer Profiles. IEEE Computer 34(2) (2001) 74–82

    Google Scholar 

  3. Bradshaw, J. M.: Software Agents. AAAI/MIT Press, Menlo Park (1997)

    Google Scholar 

  4. Chai, J., Horvath, V., Nicolov, N., Stys, M., Kambhatla, N., Zadrozny, W., Melville, P.: Natural Language Assistant. A Dialog System for Online Product Recommendation. AI Magazine 23(2) (2002) 63–75

    Google Scholar 

  5. Chen, L., Thiel U., L’Abbate, M.: Konzeptuelle Query Expansion auf der Basis des Layer-Seeds Clustering Verfahrens. In: Hammwöhner, R., Wolff, C., Womser-Hacker, C. (Hg.): Information und Mobilität — Optimierung und Vermeidung von Mobilität durch Information. Proceedings of the 8th International Symposium on Information Science. Konstanz: UVK Verlagsgesellschaft mbH (2002)

    Google Scholar 

  6. Frank, E., Witten, I.H.: Generating Accurate Rule Sets Without Global Optimization. In: Proceedings of International Conference on Machine Learning, Morgan Kaufmann, Menlo Park (1998) 144–151

    Google Scholar 

  7. Lee, W.-P., Liu, C.-H., Lu, C.-C: Intelligent Agent-based Systems for Personalized Recommendations in Internet Commerce. Expert Systems with Applications, 22(4) (2002) 275–284

    Article  Google Scholar 

  8. Pejtersen, A. M., Jensen, G., Steen, W., Jensen, H.: Visualization of Database Structures for Information Retrieval. ALT-J Association for Learning Technology Journal 2(3) (1994)

    Google Scholar 

  9. Salton, G., McGill M. J.: Introduction to Modern Information Retrieval. McGraw-Hill, Singapore (1984)

    Google Scholar 

  10. Semeraro, G., Ferilli, S., Fanizzi, N., Abbattista, F.: Learning Interaction Models in a Digital Library Service. In: Bauer, M., Gmytrasiewicz, P.J., Vassileva J. (eds.): Proceedings of 8th International Conference on User Modelling, Lecture Notes in Artificial Intelligence, Vol. 2109. Springer, Berlin Heidelberg New York (2001) 44–53

    Google Scholar 

  11. Stein, A., Gulla, J. A. and Thiel, U. User-Tailored Planning of Mixed Initiative Information-Seeking Dialogues. User Modeling and User-Adapted Interaction 9(1–2) (1999) 133–166

    Article  Google Scholar 

  12. Thiel, U., L.’Abbate, M., Paradiso, A., Stein, A., Semeraro, G., Abbattista, F., Lops, P.: The COGITO Project: Intelligent E-Commerce with Guiding Agents based on Personalized Interaction Tools. In: Gasos, J., Thoben, K.-D. (eds.): e-Business Applications: Results of Applied Research on e-Commerce, Supply Chain Management and Extended Enterprises. Section 2: eCommerce, Springer-Verlag (2002)

    Google Scholar 

  13. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Semeraro, G., Degemmis, M., Lops, P., Thiel, U., L’Abbate, M. (2003). A Personalized Information Search Process Based on Dialoguing Agents and User Profiling. In: Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2003. Lecture Notes in Computer Science, vol 2633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36618-0_49

Download citation

  • DOI: https://doi.org/10.1007/3-540-36618-0_49

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-01274-0

  • Online ISBN: 978-3-540-36618-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics