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Compromise Matching in P2P e-Marketplaces: Concept, Algorithm and Use Case

  • Manish Joshi
  • Virendrakumar C. Bhavsar
  • Harold Boley
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7080)

Abstract

A basic component of automated matchmaking is the automatic generation of a ranked list of profiles matching with the profiles of a given participant. Identifying and ranking of matching profiles among thousands of candidate profiles is a challenging task. In order to determine the degree of matching between two profiles, corresponding pairs of constraints are compared and aggregated to the overall similarity between the two profiles.

This paper describes the structure and algorithm of a proposed matchmaking system with a focus on the central notion of compromise match. A compromise match is called for when either one or both constraints within a pair are soft and moreover their values do not match exactly. Two important aspects of compromise matching are discussed, namely compromise count factor, compromise count reduction factor; furthermore their effect on ranking is described. A use case with a sample set of home rental profiles from an existing e-marketplace is employed for demonstration.

Keywords

Matchmaking in e-marketplaces soft constraints compromise match 

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References

  1. 1.
    Bhavsar, V.C., Boley, H., Lu, Y.: A Weighted-Tree Similarity Algorithm for Multi-Agent Systems in e-Business Environments. Computational Intelligence 20, 584–602 (2004)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Kuokka, D., Harada, L.: Integrating Information via Matchmaking. Journal of Intelligent Information Systems 6, 261–279 (1996)CrossRefGoogle Scholar
  3. 3.
    Liesbeth, K., Rosmalen, P., Sloep, P., Kon, M., Koper, R.: Matchmaking in Learning Networks: Bringing Learners Together for Knowledge Sharing Systems. The Netherlands Interactive Learning Environments 15(2), 117–126 (2007)CrossRefGoogle Scholar
  4. 4.
    Mohaghegh, S., Razzazi, M.R.: An Ontology Driven Matchmaking Process. World Automation Congress 16, 248–253 (2004)Google Scholar
  5. 5.
    Noia, T.D., Sciascio, E.D., Donini, F.M., Mongiello, M.: A System for Principled Matchmaking in an Electronic Marketplace. International Journal of Electronic Commerce 8, 9–37 (2004)Google Scholar
  6. 6.
    Subrahmanian, V.S., Bonatti, P., Dix, J., Eiter, T., Kraus, S., Ozcan, F., Ross, R.: Heterogenous Agent Systems. MIT Press (2000)Google Scholar
  7. 7.
    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
  8. 8.
    Veit, D., Mller, J.P., Weinhardt, C.: Multidimensional Matchmaking for Electronic Markets. International Journal of Applied Artificial Intelligence 16, 853–869 (2002)CrossRefGoogle Scholar
  9. 9.
    Joshi, M.R., Bhavsar, V.C., Boley, H.: Knowledge Representation in Matchmaking Applications. In: Akerkar, R., Sajja, P. (eds.) Advanced Knowledge Based Systems: Models Applications and Research, pp. 29–49 (2010)Google Scholar
  10. 10.
    Joshi, M.R., Bhavsar, V.C., Boley, H.: Matchmaking in P2P e-Marketplaces: Soft Constraints and Compromise Matching In: 12th International Conference on e-Commerce (ICEC 2010), pp. 148–154 (2010)Google Scholar
  11. 11.
    Joshi, M.R., Bhavsar, V.C., Boley, H.: A Knowledge Representation Model for Matchmaking System in e-Marketplaces, In: 11th International Conference on e-Commerce (ICEC 2009) pp. 362–365. ACM (2009)Google Scholar
  12. 12.
    Ragone, A., Straccia, U.V.C., Noia, T.D., Sciascio, E.D., Donini, F.M.: Vague Knowledge Bases for Matchmaking in P2P E-Marketplaces. In: 4th European Conference on The Semantic Web (ECSW 2007), pp. 414–428. Springer, Heidelberg (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Manish Joshi
    • 1
  • Virendrakumar C. Bhavsar
    • 2
  • Harold Boley
    • 3
  1. 1.Department of Computer ScienceNorth Maharashtra UniversityJalgaonIndia
  2. 2.Faculty of Computer ScienceUniversity of New BrunswickFrederictonCanada
  3. 3.Institute for Information TechnologyNational Research CouncilCanada

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