Modeling e-Procurement as Co-adaptive Matchmaking with Mutual Relevance Feedback

  • Reiko Hishiyama
  • Toru Ishida
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3371)


This paper proposes a new e-procurement model for a large number of buyers and sellers interacting via the Internet. The goal of e-procurement is to create a satisfactory match between buyers’ demand and sellers’ supply. From our real-world experience, we view e-procurement as a process of negotiation to increase the matching quality of two corresponding specifications: one for buyers’ demand and another for sellers’ supply. To model scalable e-procurement, we propose a co-adaptive matchmaking mechanism using mutual relevance feedback. In order to understand the nature of the mechanism, we have developed two types of software agents, called e-buyers and e-sellers, to simulate human buyers and sellers. Multiagent simulation results show that the matching quality is incrementally improved if agents adaptively change their specifications. A realistic example is also provided to discuss how to extend our simulation to real-world e-procurement infrastructure.


Multiagent System Relevance Feedback Software Agent Matching Quality Adaptive Agent 
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.


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  1. 1.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, Reading (1999)Google Scholar
  2. 2.
    Bayardo, R., et al.: Infosleuth: Agent-based Semantic Integration of Information in Open and Dynamic Environments. In: ACM SIGMOD Conf. on Management of Data, pp. 195–206 (1997)Google Scholar
  3. 3.
    Bichler, M., Kalagnanam, J.: Bidding Languages and Winner Determination in Multi-Attribute Auctions. IBM Research Report, RC22478, W0206-018 (2002)Google Scholar
  4. 4.
    Bichler, M.: An Experimental Analysis of Multi-attribute Auctions. Decision Support Systems 29(3), 249–268 (2000)CrossRefGoogle Scholar
  5. 5.
    Che, Y.K.: Design Competition Through Multidimensional Auctions. RAND Journal of Economics 24(4), 668–680 (1993)CrossRefGoogle Scholar
  6. 6.
    David, E., Azoulay-Schwartz, R., Kraus, S.: Protocols and strategies for automated multiattribute auctions. In: Proceedings of the first international joint conference on Autonomous agents and multiagent systems (AAMAS 2002), pp. 77–85 (2002)Google Scholar
  7. 7.
    Faratin, P., Sierra, C., Jennings, N.R.: Using Similarity Criteria to Make Issue Trade-offs in Automated Negotiations. Artificial Intelligence 142(2), 205–237 (2002)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Forker, L.B., Janson, R.L.: Ethical Practices in Purchasing. Journal of Purchasing and Materials Management 26(1), 19–26 (1990)Google Scholar
  9. 9.
    Guttman, R.H., Maes, P.: Agent-Mediated Integrative Negotiation for Retail Electronic Commerce. In: Proceedings of the Workshop on Agent Mediated Electronic Trading (1998)Google Scholar
  10. 10.
    He, M., Jennings, N.R., Leung, H.F.: On Agent-Mediated Electronic Commerce. IEEE Transactions on knowledge and data engineering 15(4), 985–1003 (2003)CrossRefGoogle Scholar
  11. 11.
    Jennings, N.R., Norman, T.J., Faratin, P., O’Brian, P., Odgers, B.: Autonomous agents for business process management. Journal of Applied Artificial Intelligence 14(2), 145–189 (2000)CrossRefGoogle Scholar
  12. 12.
    Kuokka, D., Harada, L.: Supporting Information Retrieval via Matchmaking. In: Working Notes 1995 AAAI Spring Symposium on Information Gathering in Heterogeneous, Distributed Environments, Technical Report SS-95-08. AAAI Press, Menlo Park (1995)Google Scholar
  13. 13.
    Laseter, T.: Balanced Sourcing: Cooperation and Competition in Supplier Relationships. Jossey-Bass Pulishers, San Francisco (1995)Google Scholar
  14. 14.
    Landeros, R., Reck, R., Plank, E.: Maintaining Buyer-Supplier Partnership. International Journal of Purchasing and Materials Management 31(3), 3–11 (1995)Google Scholar
  15. 15.
    Salton, G.: The SMART Retrieval System – Experiments in Automatic Document Processing. Prentice-Hall, Englewood Cliffs (1971)Google Scholar
  16. 16.
    Samtani, G.: B2B Integration – A Practical Guide to Collaborative E-commerce. Imperial College Press, London (2002)Google Scholar
  17. 17.
    Sycara, K., Widoff, S., Klusch, M., Lu, J.: LARKS: Dynamic Matchmaking Among Heterogeneous Software Agents in Cyberspace. Autonomous Agents and Multi-Agent Systems 5, 173–203 (2002)CrossRefGoogle Scholar
  18. 18.
    Tewari, G., Maes, P.: Design and Implementation of an Agent-Based Intermediary Infrastructure for Electronic Markets. In: Proceedings of the 2nd ACM conference on Electronic commerce, pp. 86–94 (2000)Google Scholar
  19. 19.
    Thompson, K.N.: Scaling Evaluative Criteria and Supplier Performance Estimates in Weighted Point Prepurchase Decision Models. International Journal of Purchasing and Materials Management 27(1), 27–36 (1991)Google Scholar
  20. 20.
    Tully, S.: Purchasing’s New Muscle. In: Fortune, February 20, pp. 75–83 (1995)Google Scholar
  21. 21.
    Veit, D., Müller, J.P., Weinhardt, C.: Multidimensional Matchmaking for Electronic Markets. Journal of Applied Artificial Intelligence 16(9-10), 853–869 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Reiko Hishiyama
    • 1
  • Toru Ishida
    • 1
  1. 1.Department of Social InformaticsKyoto UniversityKyotoJapan

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