Skip to main content
SpringerLink
Log in

Fuzzy Set Techniques in E-Service Applications

  • Chapter
Fuzzy Sets and Their Extensions: Representation, Aggregation and Models

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 220))

Abstract

E-services involve various types, delivery systems, advanced information technologies, methodologies and applications of online services that are provided by e-government, e-business, e-commerce, e-market, e-finance, e-learning systems, to name a few. They offer great opportunities and challenges for many areas, such as government, business, commerce, marketing, finance and education. E-service intelligence is a new research field that deals with fundamental roles, social impacts and practical applications of various intelligent technologies on the Internet based e-services. This chapter aims to offer a thorough introduction and systematic overview of the new field e-service intelligence mainly based on fuzzy set related techniques. It covers the state-of-the-art of the research and development in various aspects including both theorems and applications, of e-service intelligence by applying fuzzy set theory. Moreover, it demonstrates how adaptations of existing intelligent technologies benefit from the development of e-service applications in online customer decision, personalised services, web mining, online searching/data retrieval, online pattern recognition/image processing, and web-based e-logistics/planning.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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. Adomavicius G, Tuzhilin A. (2005) Personalization technologies: a process-oriented perspective. Communications of the ACM 48: 83–90

    Article  Google Scholar 

  2. Amir Y, Awerbuch B, Borgstrom RS (2000) A cost–benefit framework for online management of a metacomputing system. Decision Support Systems 28: 155–164

    Article  Google Scholar 

  3. Amoroso DL, Reinig BA (2004) Personalization management systems: minitrack introduction, Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS’04). Big Island, Hawaii

    Google Scholar 

  4. Balabanovic M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Communications of the ACM. 40: 66–72

    Article  Google Scholar 

  5. Baker FB (1992) Item response theory: Parameter estimation techniques, Marcel Dekker, New York

    MATH  Google Scholar 

  6. Bautista MM, Vila MA, Larsen H (2000) Building adaptive user profiles by a genetic fuzzy classifier with feature selection. Proceedings of the Ninth IEEE International Conference on Fuzzy Systems. pp. 308–312

    Google Scholar 

  7. Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. Madison, Wisconsin, USA

    Google Scholar 

  8. Burke R (1999) The Wasabi personal shopper: a case-based recommender systems. Proceedings of the 11th Annual Conference on Innovative Applications of Artificial Intelligence. Menlo Park, CA, pp. 844–849

    Google Scholar 

  9. Burke R (2000) Knowledge-based recommender systems. In: Kent A Ed Encyclopedia of Library and Information Systems. Marcel Dekker, New York

    Google Scholar 

  10. Burke R (2002) Hybrid recommender systems: survey and experiments. User Modelling and User-Adapted Interaction 12: 331–370

    Article  MATH  Google Scholar 

  11. Carbo J, Molina JM (2004) Agent-based collaborative filtering based on fuzzy recommendations. International Journal of Web Engineering and Technology 1: 414–426

    Article  Google Scholar 

  12. Chen CM, Duh LJ, Liu CY (2004) A personalized courseware recommendation system based on fuzzy item response theory. Proceedings of 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE’04). Taipei, Taiwan, pp. 305–308

    Google Scholar 

  13. Chidambaram L (2001) The editor’s column: why e-Service., e-Service Journal 1: 1–3

    Article  Google Scholar 

  14. Cornelis C, Guo X, Lu J, Zhang GQ (2005) A fuzzy relational approach to event recommendation. Proceedings of the Second Indian International Conference on Artificial Intelligence (IICAI-05). Pune, India, pp. 2231–2242

    Google Scholar 

  15. Delgado M, Herrera F, Herrera-Viedma E, Martìn-Bautista MJ, Martìnez L, Vila MA (2002) A communication model based on the 2-tuple fuzzy linguistic representation for a distributed intelligent agent system on Internet. Soft Computing 6: 320–328

    MATH  Google Scholar 

  16. Delgado M, Herrera F, Herrera-Viedma E, Martìn-Bautista MJ, Vila MA (2001) Combining linguistic information in a distributed intelligent agent model for information gathering on the Internet. In: Wang PP Ed Computing with Word. John Wiley & Son, pp. 251–276

    Google Scholar 

  17. Drinjak J, Altmann G, Joyce P (2001) Justifying investments in electronic commerce. Proceedings of the Twelfth Australia conference on Information Systems. Coffs Habour, Australia, pp. 187–198

    Google Scholar 

  18. Dubois D, Fargier H, Prade H (1994) Propagation and satisfaction of flexible constraints. In: Yager RR, Zadeh LA (Eds) Fuzzy Sets, Neural Networks arid Sott Computing. Van Nostrand Reinhold, New York, pp. 166–187

    Google Scholar 

  19. Eirinaki M, Vazirgiannis M (2003) Web mining for web personalization. ACM Transactions on Internet Technology 3: 1–27

    Article  Google Scholar 

  20. Enrique H, Eduardo P, Marìa DO, Juan CH, Yusef HM (2003) Evaluating the informative quality of web sites by fuzzy computing with words. In: Zhong N Ed Advances in Web Intelligence: First International AtlanticWeb Intelligence Conference AWIC 2003. Springer Berlin/Heidelberg, New York, pp. 62–72

    Google Scholar 

  21. Giaglis GM, Paul RJ, Doukidis GI (1999) Dynamic modelling to assess the business value of electronic commerce. International Journal of Electronic Commerce 3: 35–51

    Google Scholar 

  22. Guo X, Lu J (2006) Intelligent e-government services with recommendation techniques. International Journal of Intelligent Systems, Special issue on E-service intelligence. Accepted

    Google Scholar 

  23. Hahn J, Kauffman RJ (2002) Evaluating selling web site performance from a business value perspective. Proceedings of International conference on e-Business. Beijing, China, pp. 435–443

    Google Scholar 

  24. Herrera-Viedma E, Herrera F, Martìnez L, Herrera JC, Lòpez AG (2004) Incorporating filtering techniques in a fuzzy multi-agent model for gathering of information on the web. Fuzzy Sets and Systems 148: 61–83

    Article  MATH  MathSciNet  Google Scholar 

  25. Herrera-Viedma E, Peis E, Morales-Del-Castillo JM, Anaya K (2006) Improvement of web-based service information systems using fuzzy linguistic techniques and semantic web technologies. In: Lu J, Ruan D, Zhang GQ (Eds) E-Service Intelligence: Methodologies, Technologies and Applications. Springer, Heideberg

    Google Scholar 

  26. Hong T, Kuo C, Chi S (1999) Mining fuzzy sequential patterns from quantitative data. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. pp. 962–966

    Google Scholar 

  27. Hsu T, Chu KM, Chan HC (2002) The fuzzy clustering on market segment. Proceedings of the Ninth IEEE International Conference on Fuzzy Systems. pp. 621–626

    Google Scholar 

  28. Jain V, Krishnapuram R (2001) Applications of fuzzy sets in personalization for e-commerce. Proceedings of IFSA-NAFIPS 2001 Conference. pp. 263–268

    Google Scholar 

  29. Karypis G (2001) Evaluation of item-based top-N recommendation algorithms. Proceedings of the ACM 10th International Conference on Information and Knowledge Management. Atlanta, Georgia, pp. 247–254

    Google Scholar 

  30. Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and applications. Upper Saddle River, Prentice Hall, NJ

    MATH  Google Scholar 

  31. Kowalczyk R (1999) On linguistic fuzzy constraint satisfaction problems. In: Zadeh LA, Kacprzyk J (Eds) Computing with Words in Intelligent Information Systems. Kluwer, pp. 166–187

    Google Scholar 

  32. Kowalczyk R, Bui V (2000) FeNAs: a fuzzy e-Negotiation agents system. Proceedings of the Conference on Computational Intelligence for Financial Engineering. NY, pp. 26–29

    Google Scholar 

  33. Krishnapuram R, Joshi A, Nasraoui O, Yi, L (2001) Low complexity fuzzy relational clustering algorithms for web mining. IEEE Transactions on Fuzzy Systems 9: 595–607

    Article  Google Scholar 

  34. Kuok C, Fu A, Wong MH (1998) Mining fizzy association rules in databases. SIGMOD Record 27: 41–46

    Article  Google Scholar 

  35. Lee C, Seddon P, Corbitt B (1999) Evaluating business value of internet-based business-to-business electronic commerce. Proceedings of 10th Australasian Conference on Information Systems. pp. 508–519

    Google Scholar 

  36. Lee YW, Strong DM, Kahn BK, Wang RY (2002) AIMQ: a methodology for information quality assessment. Inf. & Manag. 40: 133–146

    Article  Google Scholar 

  37. Lin C (2003) A critical appraisal of customer satisfaction and e-commerce. Managerial Auditing Journal 18: 202–212

    Article  Google Scholar 

  38. Lu J (2003) A model for evaluating e-commerce based on cost/benefit and customer satisfaction. Journal of Information Systems Frontiers 5: 265–277

    Article  Google Scholar 

  39. Lu J, Lu Z (2004) Development, distribution and evaluation for online tourism services in China. Electronic Commerce Research Journal 4: 221–239

    Article  Google Scholar 

  40. Lu J, Ruan D, Zhang GQ (2006a) E-service intelligence: methodologies, technologies and applications, Springer, Heideberg, to appear

    Google Scholar 

  41. Lu J, Ruan D, Zhang GQ, Zimmermann HJ (2006) Editorial preface. International Journal of Intelligent Systems. Special Issue on E-Service Intelligence, to appear

    Google Scholar 

  42. Lu J, Tang S, Mccullough G (2001) An assessment for internet-based electronic commerce development in businesses of New Zealand electronic markets. International Journal of Electronic Commerce and Business Media 11: 107–115

    Google Scholar 

  43. Lu J, Zhang GQ (2003) Cost benefit factor analysis in e-services. International Journal of Service Industry Management 14: 570–595

    Article  Google Scholar 

  44. Lu J, Zhang GQ, Wu F (2005) Web-based multi-criteria group decision support system with linguistic term processing function. The IEEE Intelligent Informatics Bulletin 5: 35–43

    Google Scholar 

  45. Manchala DW (2000) E-commerce trust metrics and models. IEEE Internet Computing 4: 36–44

    Article  Google Scholar 

  46. Mela C, Lehmann DR (1995) Using fuzzy set theoretic techniques to identify preference rules from interactions in the linear model: an empirical study. Fuzzy Sets and Systems 71: 165–181

    Article  Google Scholar 

  47. Mobasher B, Dai H, Luo T, Sung Y, Nakagawa M, Wiltshire J (2000) Discovery of aggregate usage profiles for web personalization. Proceedings of the Web Mining for E-Commerce Workshop, WebKDD. Boston, USA

    Google Scholar 

  48. Mooney R, Roy L (1999) Content-based book recommending using learning for text categorization. Proceedings of the ACM SIGIR’99 Workshop on Recommender Systems: Algorithms and Evaluation. Berkeley, CA, USA

    Google Scholar 

  49. Nasraoui O, Petenes C (2003) An intelligent web recommendation engine based on fuzzy approximate reasoning. Proceedings of the IEEE International Conference on Fuzzy Systems. pp. 1116–1121

    Google Scholar 

  50. Nefti S, Meziane F, Kasiran K (2005) A fuzzy trust model for e-commerce. Proceedings of Seventh IEEE International Conference on E-Commerce Technology (CEC’05). pp. 401–404

    Google Scholar 

  51. Ng H, Pan YJ, Wilson TD (1998) Business use of the world wide web: a report on further investigations. International Journal of Management 18: 291–314

    Article  Google Scholar 

  52. Ngai EWT, Wat FKT (2005) Fuzzy decision support system for risk analysis in e-commerce development. Decision Support Systems 40: 235–255

    Article  Google Scholar 

  53. Nikravesh M (2006) Concept-based semantic web search and Q&A. In: Lu J, Ruan D, Zhang GQ (Eds) E-Service Intelligence: Methodologies, Technologies and Applications. Springer, Heideberg, to appear

    Google Scholar 

  54. Nishio C, Shiizuka H (1995) Competitive market structures constructed from brand-switching data by fuzzy structural modeling. Proceedings of IEEE International Conference on Fuzzy Systems. pp. 819–824

    Google Scholar 

  55. Perny P, Zucker JD (1999) Collaborative filtering methods based on fuzzy preference relations. Proceedings of the EUROFUSE-SIC. pp. 279–285

    Google Scholar 

  56. Perny P, Zucker JD (2001) Preference-based search and machine learning for collaborative filtering: the ‘Film-Conseil’ movie recommender system. Revue I3, 1: 1–40

    Google Scholar 

  57. Ruan D (1997) Intelligent hybrid systems. Kluwer Academic Publishers, Boston

    MATH  Google Scholar 

  58. Schwab I, Pohl W, Koychev I (2000) Learning to recommend from positive evidence. Proceedings of the 5th International Conference on Intelligent User Interfaces. New Orleans, LA, USA

    Google Scholar 

  59. Setnes M, Kaymak U (2001) Fuzzy modeling of client preference from large data sets: an application to target selection in direct marketing. IEEE Transactions on Fuzzy Systems 9: 153–163

    Article  Google Scholar 

  60. Smith AG (2001) Applying evaluation criteria to New Zealand government websites. International Journal of Information Management 21: 137–149

    Article  Google Scholar 

  61. Srinivasan S, Anderson R, Ponnavolu K (2002) Customer loyalty in e-commerce: an exploration of its antecedents and consequences. Journal of Retailing 78: 41–50

    Article  Google Scholar 

  62. Viswanathan M, Childers TL (1999) Understanding how product attributes enhance product categorization: development and validation of fuzzy set-based measures of gradednesss in product categories. Journal of Marketing Research 36: 75–94

    Article  Google Scholar 

  63. Wade MR, Nevo S (2005) Development and validation of a perceptual instrument to measure e-commerce performance. International Journal of Electronic Commerce 10: 123

    Article  Google Scholar 

  64. Yager RR (2000) Targeted e-commerce marketing using fuzzy intelligent agents. IEEE Intelligent Systems 15: 42–45

    Article  Google Scholar 

  65. Yager RR (2003) Fuzzy logic methods in recommender systems. Fuzzy Sets and Systems 136: 133–149

    Article  MATH  MathSciNet  Google Scholar 

  66. Zadeh LA (1965) Fuzzy sets. Inform & Control 8: 338–353

    Article  MATH  MathSciNet  Google Scholar 

  67. Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Man Cybernetics 3: 28–44

    MATH  MathSciNet  Google Scholar 

  68. Zadeh LA (1975) The concept of a linguistic variable and its applications to approximate reasoning, Part I Information Sciences 8: 199–249, Part II. Information Sciences 8: 301–357, Part III. Information Sciences 9: 43–80

    Google Scholar 

  69. Zadeh LA (1996) Fuzzy logic = computing with words. IEEE Transactions on Fuzzy Systems 4: 103–111

    Article  Google Scholar 

  70. Zeng C, Xing CX, Zhou LZ, Zheng XH (2004) Similarity measure and instance selection for collaborative filtering. International Journal of Electronic Commerce 8: 115–129

    Google Scholar 

  71. Zhang P, Dran VG (2000) Satisfiers and dissatisfiers: a two-factor model for website design and evaluation. Journal of American Association for Information Science (JASIS) 51: 1253–1268

    Article  Google Scholar 

Download references

Authors
  1. Jie Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Da Ruan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guangquan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Automatics and Computation, Universidad Pública de Navarra, Campus Arrosadia s/n, 31006 Pamplona, Spain

    Humberto Bustince

  2. Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada, Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain

    Francisco Herrera

  3. Faculty of Mathematics, Universidad Complutense, Plaza de las Ciencias 3, 28040 Madrid, Spain

    Javier Montero

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Lu, J., Ruan, D., Zhang, G. (2008). Fuzzy Set Techniques in E-Service Applications. In: Bustince, H., Herrera, F., Montero, J. (eds) Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Studies in Fuzziness and Soft Computing, vol 220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73723-0_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73723-0_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73722-3

  • Online ISBN: 978-3-540-73723-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation