Soft Computing in E-Commerce

  • Raghu Krishnapuram
  • Manoj Kumar
  • Jayanta Basak
  • Vivek Jain
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2275)


Electronic commerce (or e-commerce for short) is a new way of conducting, managing, and executing business using computer and telecommunication networks. There are two main paradigms in ecommerce, namely, business-to-business (B2B) e-commerce and businessto- consumer (B2C) e-commerce. In this paper, we outline the various issues involved in these two types of e-commerce and suggest some ways in which soft computing concepts can play a role.


Association Rule Fuzzy System Fuzzy Cluster Soft Computing Mining Association Rule 
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.
    R. Kalakota and A. B. Whinston, Frontiers of Electronic Commerce, Addison Wesley, Reading, 1999.Google Scholar
  2. 2.
    M. Klush, “Information agent technology for the internet: A survey,” Nature, vol. 36, no. 3, pp. 337–372, 2001.Google Scholar
  3. 3.
    K. Decker, K. Sycara, and M. Williamson, “Intelligent adaptive information agents,” J. of Intelligent Information Systems, vol. 9, pp. 239–260, 1997.CrossRefGoogle Scholar
  4. 4.
    V. N. Gudivada, “Information retrieval on the world wide web,” IEEE Internet Computing, vol. 1, no. 5, 1997.Google Scholar
  5. 5.
    R. Guttman, A. Moukas, and P. Maes, “Agents as mediators in electronic commerce,” in Intelligent Information Agents, M. Klush (Ed), Springer, Berlin, 1999, p. Chapter 6.Google Scholar
  6. 6.
    P. Noriega and C. Sierra (Eds), “Proc. int. conf. agent mediated electronic trading (amet-98),” in Lecture Notes in Artificial Intelligence, Springer, Berlin, 1998, vol. 1571.Google Scholar
  7. 7.
    V. Jain and R. Krishnapuram, “Applications of fuzzy sets in personalization for e-commerce,” in Proc. IFSA-NAFIPS Conference, Vancouver, Canada, 2001.Google Scholar
  8. 8.
    J. Basak and M. Kumar, “E-commerce and soft computing: Scopes of research,” IETE Technical Review, vol. 18, July–August 2001.Google Scholar
  9. 9.
    D. Riecken-Guest-Editor, “Special issue on personalization,” Comm. of the ACM, vol. 43, no. 9, Sept. 2000.Google Scholar
  10. 10.
    M. J. A. Berry and G. S. L. Lino., Mastering Data Mining: the Art and Science of Customer Relationship Management, John Wiley, New York, 2000.Google Scholar
  11. 11.
    G. Adomavicious and A. Tuzhilim, “Using data mining methods to build customer profiles,” IEEE Computer, vol. 34, no. 2, pp. 74–82, 2001.Google Scholar
  12. 12.
    D. Hackerman J.S. Breese and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” in Proc. 14th Conf. Uncertainty in Artificial Intelligence, Madison, WI, 1998.Google Scholar
  13. 13.
    M. Pazzani, “A framework for collaborative, content-based and demographic filtering,” Artificial Intelligence Review, pp. 393–408, December 1999.Google Scholar
  14. 14.
    K. S. Natarajan et al., “A framework for collaborative, content-based and demographic filtering,” IBM Technical Disclosure Bulletin, vol. 29, no. 10, pp. 4468–4471, 1987.Google Scholar
  15. 15.
    R. P. McAfee and J. McMillan, “Auctions and bidding,” Journal of Economic Literature, vol. 25, 1987.Google Scholar
  16. 16.
    M. Kumar and S. I. Feldman, “Internet auctions,” in Proc. Third USENIX workshop on Electronic Commerce, Boston, 1998, pp. 49–60.Google Scholar
  17. 17.
    M. Kumar and S. I. Feldman, “Business negotiations on the internet,” in Proc. Inet-98, Geneva, Switzerland, 1998.Google Scholar
  18. 18.
    N. Matos, C. Sierra, and N. Jennings, “Determining successful negotiation strategies: An evolutionary approach,” in Proc. ICMAS-98, Paris, 1998.Google Scholar
  19. 19.
    K. Sycara and D. Zheng, “Benefits of learning in negotiation,” in Proc. AAAI, Providence, 1997.Google Scholar
  20. 20.
    M. Pazzani, L. Nguyen, and S. Mantik, “Learning from hotlists and coldlists: Towards a WWW information filtering and seeking agent,” in Proc. of Tools with Artificial Intelligence, Washington, DC, 1995, pp. 492–495.Google Scholar
  21. 21.
    A. Pretschner and S. Gauch, “Personalization on the web,” Technical Report, Department of Elecrtical Engineering and Computer Science, The University of Kansas, vol. ITTC-FY2000, no. TR-13591-01, pp. 1–31, Dec. 1999.Google Scholar
  22. 22.
    P.H. Chan, “A non-invasive learning approach in building web user profiles,” in 5th KDD-99 Workshop on Web Usage Analysis and User Profiling, San Diego, USA, Aug. 1999, ACM.Google Scholar
  23. 23.
    D.H. Widyantoro, T.R. Ioerger, and J. Yen, “An adaptive algorithm for learning changes in user interests,” in Proc. CIKM, Kansas City, Nov. 1999, pp. 405–412.Google Scholar
  24. 24.
    B. Mobasher, H. Dai, T. Luo, Y. Sung, M. Nakagawa, and J. Wiltshire, “Discovery of aggregate usage profiles for web personalization,” in Proc. of the Web Mining for E-Commerce Workshop, WebKDD, Boston, USA, Aug. 2000.Google Scholar
  25. 25.
    R. Krishnapuram, O. Nasraoui, A. Joshi, and L. Yi, “Low-complexity fuzzy relational clustering algorithms for web mining,” IEEE Trans. on Fuzzy Systems, vol. 9, no. 4, pp. 595–607, 2001.CrossRefGoogle Scholar
  26. 26.
    O. Nasraoui and R. Krishnapuram, “Mining web access logs using a relational clustering algorithm based on a robust estimator,” in Proc. of the Eighth Intl. WWW Conf., Toronto, 1999, pp. 40–41.Google Scholar
  27. 27.
    O. Nasraoui, R. Krishnapuram, and A. Joshi, “Relational clustering based on a new robust estimator with application to web mining,” in Proc. of the NAFIPS Workshop Intl. WWW Conf., New York City, 1999, pp. 705–709.Google Scholar
  28. 28.
    M.J. Martin-Bautista, M.A. Vila, and H.L. Larsen, “Building adaptive user profiles by a genetic fuzzy classifier with feature selection,” in The Ninth IEEE Intl. Conf. on Fuzzy Systems, 2000, vol. 1, pp. 308–312.Google Scholar
  29. 29.
    P.E. Green, D.S. Tull, and G. Albaum, Research for Marketing Decisions, Prenctice Hall, N.J., USA, 1988.Google Scholar
  30. 30.
    T.H. Hsu, K.M. Chu, and H.C. Chan, “The fuzzy clustering on market segment,” in The Ninth IEEE Intl. Conf. on Fuzzy Systems, May 2000, vol. 2, pp. 621–626.Google Scholar
  31. 31.
    M. Viswanathan and T. L. Childers, “Understanding how product attributes infuence product categorization: development and validation of fuzzy set-based measures of gradednesss in product categories,” Journal of Marketing Research, vol. XXXVI, pp. 75–94, Feb. 1999.Google Scholar
  32. 32.
    R. Agrawal, T. Imielinski, and A. Swami, “Mining association rules between sets of items in large databases,” in Proc. of ACM SIGMOD Int’l Conf. Management of Data, Washington, D.C, May 1993, pp. 207–216.Google Scholar
  33. 33.
    R. Agrawal and R. Srikant, “Fast algorithms for mining association rules,” in Proc. of Int’l Conf. Very Large Data Bases, Santiago, Chile, Sept. 1994, pp. 487–499.Google Scholar
  34. 34.
    R. Agrawal and J.C. Shafer, “Parallel mining of association rules: Design, implementation, and experience,” IEEE. Trans. Pattern Analysis and Machine Intelligence, vol. 8, pp. 962–969, 1996.Google Scholar
  35. 35.
    R.J. Bayardo-Jr. and R. Agrawal, “Mining the most interesting rules,” in KDD, San Diego, USA, 1999.Google Scholar
  36. 36.
    C.C. Aggarwal, J.L. Wolf, and P.S. Yu, “A method for similarity indexing of market basket data,” in ACM SIGMOD Conf. on Management of Data, Philadelphia, PA USA, May 1999, pp. 407–418.Google Scholar
  37. 37.
    B. Kitts, D. Freed, and M. Vrieze, “Cross-sell: A fast promotion-tunable customeritem recommendation method based on conditionally independent probabilities,” in KDD, Boston, USA, Aug. 2000, pp. 437–446.Google Scholar
  38. 38.
    W.H. Au and K.C.C. Chan, “An effiective algorithm for discovering fuzzy rules in relational databases,” in Proc. of The IEEE Intl. Conf. on Fuzzy Systems, 1998, vol. 2, pp. 1314–1319.Google Scholar
  39. 39.
    W.H. Au and K.C.C. Chan, “Farm: a data mining system for discovering fuzzy association rules,” in Proc. of the IEEE Intl. Fuzzy Systems Conf., 1999, vol. 3, pp. 1217–1222.Google Scholar
  40. 40.
    J.J. Mazlack, “Approximate clustering in association rules,” in 19th Intl. Conf. of the North American Fuzzy Information Processing Society, 2000, pp. 256–260.Google Scholar
  41. 41.
    C.M. Kuok, A. Fu, and M. H. Wong, “Mining fuzzy association rules in databases,” SIGMOD Record, vol. 27, no. 1, pp. 41–46, March 1998.CrossRefGoogle Scholar
  42. 42.
    A.W.C. Fu, M.H. Wong, S.C. Sze, W.C. Wong, W.L. Wong, and W.K. Yu, “Finding fuzzy sets for the mining of fuzzy association rules for numerical attributes,” in 1 st Intl. Symposium on Intelligent Data Engineering and Learning (IDEAL’98), Oct. 1998, vol. 2, pp. 263–268.Google Scholar
  43. 43.
    R. Agrawal and R. Srikant, “Mining sequential patterns,” in Proc. of Int’l Conf. Data Eng, Taipei, Taiwan, March 1995, pp. 3–14.Google Scholar
  44. 44.
    T.P. Hong, C.S. Kuo, and S.C. Chi, “Mining fuzzy sequential patterns from quantitative data,” in IEEE Intl. Conf. on Systems, Man, and Cybernetics, 1999, vol. 3, pp. 962–966.Google Scholar
  45. 45.
    P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “Grouplens: an open architecture for collaborative filtering of netnews,” in Proc. of ACM Conf. on Computer Supported Cooperative Work, Chapel Hill, USA, Oct. 1994, pp. 175–186.Google Scholar
  46. 46.
    H. Kautz, B. Selman, and M. Shah, “Referal Web: Combining social networks and collaborative filtering,” Comm. of the ACM, vol. 40, no. 3, pp. 63–65, 1997.CrossRefGoogle Scholar
  47. 47.
    U. Shardanand and P. Maes, “Social information filetering: Algorithms for automating ‘word of mouth’,” in Proc. of CHI’95 Conf. on Human Factors in Computing Systems, New York, 1995, ACM Press.Google Scholar
  48. 48.
    P.M. Guadagni and J.D.C. Little, “A logit model of brand choice calibrated on scanner data,” Marketing Science, vol. 2, no. 3, pp. 203–238, Summer 1983.Google Scholar
  49. 49.
    P. E. Rossi, R. E. McCulloch, and G. M. Allenby, “The value of purchase history data in target marketing,” Marketing Science, vol. 15, no. 4, pp. 321–340, Winter 1996.CrossRefGoogle Scholar
  50. 50.
    R. Krishnapuram and J. Lee, “Fuzzy-connective-based hierarchical aggregation networks for decision making,” Fuzzy Sets and Systems, vol. 46, no. 1, pp. 11–27, Feb. 1992.CrossRefMathSciNetGoogle Scholar
  51. 51.
    M. Grabisch, “On equivalence classes of fuzzy connectives: The case of fuzzy integrals,” IEEE Trans. on Fuzzy Systems, vol. 8, no. 1, pp. 96–109, 1995.CrossRefGoogle Scholar
  52. 52.
    Y. Choi, D. Kim, and R. Krishnapuram, “Relevance feedback for content-based image retrieval using the choquet integral,” in IEEE Conf. on Multimedia and Expo, New York City, July–Aug 2000.Google Scholar
  53. 53.
    C.F. Mela and D.R. Lehmann, “Using fuzzy set theoretic techniques to identify preference rules from interactions in the linear model: an empirical study,” Fuzzy Sets and Systems, vol. 71, no. 2, pp. 165–181, 1995.CrossRefGoogle Scholar
  54. 54.
    R.R. Yager, “Fuzzy modeling for intelligent decision making under uncertainty,” IEEE Trans. on Systems, Man and Cybernetics, Part B Cybernetics, vol. 30, no. 1, pp. 60–70, Feb. 2000.CrossRefGoogle Scholar
  55. 55.
    M. Setnes, U. Kaymak, and H.R.V.N. Lemke, “Fuzzy target selection in direct marketing,” in Proc. of the IEEE/IAFE/INFORMS Conf. on Computational Intelligence for Financial Engineering (CIFEr), 1998, pp. 92–97.Google Scholar
  56. 56.
    M. Setnes and U. Kaymak, “Fuzzy modeling of client preference from large data sets: an application to target selection in direct marketing,” IEEE Trans. on Fuzzy Systems, to appear in, 2001.Google Scholar
  57. 57.
    S. Park, “Neural networks and customer grouping in e-commerce: a framework using fuzzy art,” in IEEE Academia/Industry Working Conf. Proc. on Research Challenges, 2000, pp. 331–336.Google Scholar
  58. 58.
    R.R. Yager, “Fuzzy methods in e-commerce,” in 18th Intl. Conf. of the North American Fuzzy Information Processing Society, 1999, pp. 5–11.Google Scholar
  59. 59.
    R.R. Yager, “Targeted e-commerce marketing using fuzzy intelligent agents,” IEEE Intelligent Systems, vol. 15, no. 6, pp. 42–45, Nov. 2000.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Raghu Krishnapuram
    • 1
  • Manoj Kumar
    • 1
  • Jayanta Basak
    • 1
  • Vivek Jain
    • 1
  1. 1.IBM India Research Lab, Block 1Indian Institute of TechnologyNew DelhiIndia

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