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Personalized Conversational Case-Based Recommendation

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Advances in Case-Based Reasoning (EWCBR 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1898))

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Abstract

In this paper, we describe the Adaptive Place Advisor, a user adaptive, conversational recommendation system designed to help users decide on a destination, specifically a restaurant. We view the selection of destinations as an interactive, conversational process, with the advisory system inquiring about desired item characteristics and the human responding. The user model, which contains preferences regarding items, attributes, values, value combinations, and diversification, is also acquired during the conversation. The system enhances the user’s requirements with the user model and retrieves suitable items from a case-base. If the number of items found by the system is unsuitable (too high, too low) the next attribute to be constrained or relaxed is selected based on the information gain associated with the attributes. We also describe the current status of the system and future work.

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References

  1. Aha D., Breslow L., ‘Refining Conversational Case Libraries’, in Leake D., Plaza E. (eds.) ‘Case-Based Reasoning Research and Development, Second International Conference on Case-Based Reasoning ICCBR 1997’, pp. 267–278, Springer Verlag, Berlin 1997.

    Google Scholar 

  2. Billsus, D., Pazzani, M.(1998). ‘Learning collaborative information filters’, Proceedings of the Fifteenth International Conference on Machine Learning (pp. 46–54). Madison, WI: Morgan Kaufmann.

    Google Scholar 

  3. Bonzano A., Cunningham P., Smyth B., ‘Using Introspective Learning to Improve Retrieval in CBR: A Case Study in Air Traffic Contol’, in Leake D., Plaza E. (eds.) ‘Case-Based Reasoning Research and Development, Second International Conference on Case-Based Reasoning ICCBR 1997’, Springer Verlag, Berlin 1997.

    Google Scholar 

  4. Burke, R., Hammond, K., and Young, B. ‘Knowledge-based navigation of complex information spaces’, In Proceedings of the 13th National Conference on Artificial Intelligence AAAI96, pp. 462–468. American Association for Artificial Intelligence, 1996.

    Google Scholar 

  5. Burke R., ‘The Wasabi Personal Shopper: A Case-Based Recommender System’, in: Proceedings of the 16th National Conference on Artificial Intelligence AAAI99, American Association for Artificial Intelligence, 1999.

    Google Scholar 

  6. Elio R., Haddadi A., ‘Dialog management for an adaptive database assistant’, Technical Report 98-3, Daimler-Benz research and Technology Center, Palo Alto, CA, 1998.

    Google Scholar 

  7. Elio R., Haddadi A., ‘On abstract task models and conversation policies’, in Proceedings of the Agents’99 Workshop on Specifying and Implementing Conversation Policies,Seattle, WA, 1999.

    Google Scholar 

  8. Fiechter C. N., Rogers S., ‘Learning Subjective Functions with Large Margins’, Proceedings of the Seventeenth International Conference on Machine Learning, June 29–July 2, 2000, Stanford University, pp. 287–294, Morgan Kaufmann Publishers, 2000

    Google Scholar 

  9. Göker M. H., Roth-Berghofer T., ‘The development and utilization of the case-based help-desk support system HOMER’, Engineering Applications of Artificial Intelligence 12 (1999), pp. 665–680, Pergamon-Elsevier Science Ltd. 1999.

    Google Scholar 

  10. Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., and Riedl, J..’GroupLens: Applying Collaborative Filtering to Usenet News’, Communications of the ACM40, 3(1997), 77–87.

    Article  Google Scholar 

  11. Lang K, ‘NewsWeeder: Learning to filter news’, in ‘Proceedings of the Twelfth Conference on Machne Learning’, pp. 331–339, Lake Tahoe, CA, Morgan Kaufmann, 1995.

    Google Scholar 

  12. Langley, P. ‘Machine learning for adaptive user interfaces’, in: ‘KI97: Proceedings of the 21st German Annual Conference on Artificial Intelligence’, pp. 53–62. Freiburg, Germany: Springer, 1997.

    Google Scholar 

  13. Langley, P., Thompson, C., Elio, R., Haddadi, A., ‘An adaptive conversational interface for destination advice’ in: ‘Proceedings of the Third International Workshop on Cooperative Information Agents’, pp. 347–364, Uppsala, Sweden, 1999.

    Google Scholar 

  14. Linden G., Hanks S., Lesh N, ‘Interactive Assesment of User Preference Models: The Automated Travel Assistant’, in Jameson A., Paris C., Tasso C. (eds.), ‘User Modelling: Proceedings of the Sixth International Conference, UM97’, Springer Verlag, Vienna, 1997.

    Google Scholar 

  15. Pazzani M., Muramatsu J., Billsus D., ‘Syskill and Webert: Identifying interesting web sites’, in ‘Proceedings of the 13th National Conference on Artificial Intelligence’, pp. 54–61, American Association for Artificial Intelligence, 1996.

    Google Scholar 

  16. Resnick P., Varian H. (eds), ‘Recommender Systems’, Communications of the ACM, Vol. 40, No. 3, March 1997.

    Google Scholar 

  17. Rich, E., ‘User modeling via stereotypes’, Cognitive Science, 3, 329–354, 1979.

    Article  Google Scholar 

  18. Searle J., ‘Speech Acts’, New York, Cambridge University Press, 1969.

    Google Scholar 

  19. Smyth B., Cotter P., ‘Surfing the Digital Wave, Generating Personalised TV Listings using Collaborative, Case-Based Recommendation’, in: Althoff K.D., Bergmann R., Branting K. (eds), ‘Case-Based Reasoning Research and Development, Proceedings of the Third International Conference on Case-Based Reasoning ICCBR99’, pp. 561–571, Springer Verlag, Berlin 1999.

    Google Scholar 

  20. Wettschereck D., Aha D., ‘Weighting Features’, in M. Veloso& A. Aamodt eds., “Advances in Case-Based Reasoning, Proceedings of the First International Conference on Case-Based Reasoning ICCBR95”, pp347–358, Springer Verlag, Berlin, 1995

    Google Scholar 

  21. Zhang Z., Yang Q., ‘Towards Lifetime Maintenance of Case Base Indexes for Continual Case Based Reasoning’, in ‘Proceedings of the 1998 International Conference on AI Methodologies, Systems and Applications (AIMSA98)’, Bulgaria, October 1998.

    Google Scholar 

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Author information

Authors and Affiliations

  1. DaimlerChrysler Research & Technology, 1510 Page Mill Road, Palo Alto, CA, 94304

    Mehmet H. Göker

  2. Center for the Study of Language and Information, Stanford University, Stanford, CA, 94305-4115

    Cynthia A. Thompson

Authors
  1. Mehmet H. Göker
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  2. Cynthia A. Thompson
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Editor information

Editors and Affiliations

  1. Centre for Scientific and Technological Research (ITC-irst), Istituto Trention di Cultura, via Sommarive 18, 38050, Povo (Trento), Italy

    Enrico Blanzieri

  2. DISTA, University of Eastern Piedmont “Amedeo Avogadro”, C.so Borsalino 54, 15100, Alessandria, Italy

    Luigi Portinale

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© 2000 Springer-Verlag Berlin Heidelberg

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Göker, M.H., Thompson, C.A. (2000). Personalized Conversational Case-Based Recommendation. In: Blanzieri, E., Portinale, L. (eds) Advances in Case-Based Reasoning. EWCBR 2000. Lecture Notes in Computer Science, vol 1898. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44527-7_10

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  • DOI: https://doi.org/10.1007/3-540-44527-7_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67933-2

  • Online ISBN: 978-3-540-44527-2

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