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Journal of Intelligent Information Systems

, Volume 27, Issue 2, pp 95–115 | Cite as

Fuzzy methods for case-based recommendation and decision support

  • Didier Dubois
  • Eyke HüllermeierEmail author
  • Henri Prade
Article

Abstract

The paper proposes two case-based methods for recommending decisions to users on the basis of information stored in a database. In both approaches, fuzzy sets and related (approximate) reasoning techniques are used for modeling user preferences and decision principles in a flexible manner. The first approach, case-based decision making, can principally be seen as a case-based counterpart to classical decision principles well-known from statistical decision theory. The second approach, called case-based elicitation, combines aspects from flexible querying of databases and case-based prediction. Roughly, imagine a user who aims at choosing an optimal alternative among a given set of options. The preferences with respect to these alternatives are formalized in terms of flexible constraints, the expression of which refers to cases stored in a database. As both types of decision support might provide useful tools for recommender systems, we also place the methods in a broader context and discuss the role of fuzzy set theory in some related fields.

Keywords

Case-based reasoning Recommender systems Fuzzy sets Approximate reasoning Decision making Nearest neighbor estimation 

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References

  1. Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning, 6(1), 37–66.Google Scholar
  2. Bellmann, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17, 141–164.MathSciNetCrossRefGoogle Scholar
  3. Bosc, P., & Pivert, O. (1992). Some approaches for relational databases flexible querying. Journal of Intelligent Information Systems, 1, 323–354.CrossRefGoogle Scholar
  4. Bosc, P., & Pivert, O. (1995). SQLf: A relational database language for fuzzy querying. IEEE Transactions on Fuzzy Systems, 3(1), 1–17.MathSciNetCrossRefGoogle Scholar
  5. Bosc, P., Lietard, L., & Prade, H. (1998). An ordinal approach to the processing of fuzzy queries with flexible quantifiers. In A. Hunter & S. Parsons (Eds.), Applications of uncertainty formalisms, volume 1455 of Lecture Notes in Computer Science (pp. 58–75). Berlin: Springer-Verlag.Google Scholar
  6. Brafmann, R., & Tennenholtz, M. (1996). On the foundations of qualitative decision theory. In Proceedings AAAI-96, 13th National Conference on Artificial Intelligence (pp. 1291–1296). Cambridge, USA: AAAI-Press.Google Scholar
  7. Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collarborative filtering. In Proceedings UAI–98. Madison, WI.Google Scholar
  8. Chow, C. K. (1970). On optimum recognition error and reject tradeoff. IEEE Transactions on Information Theory, IT-16, 41–46.CrossRefGoogle Scholar
  9. Cross, V., & Sudkamp, T. (2002). Similarity and computability in fuzzy set theory: Assessments and applications, Studies in Fuzziness and Soft Computing, volume 93, Heidelberg: Physica Verlag.Google Scholar
  10. Dasarathy, B. V. (Ed.) (1991). Nearest Neighbor (NN) norms: NN pattern classification techniques. Los Alamitos, California: IEEE Computer Society Press.Google Scholar
  11. de Calmés, M., Dubois, D., Hüllermeier, E., Prade, H., & Sédes, F. (2003). Flexibility and case-based evaluation in querying: An illustration in an experimental setting. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(1), 43–66.zbMATHCrossRefGoogle Scholar
  12. Dubois, D., & Prade, H. (1995). Possibility theory as a basis for qualitative decision theory. In Proceedings IJCAI-95, 14th International Joint Conference on Artificial Intelligence (pp. 1924–1930). Montreal.Google Scholar
  13. Dubois, D., & Prade, H. (1996). Semantics of quotient operators in fuzzy relational databases. Fuzzy Sets and Systems, 78, 89–93.MathSciNetCrossRefGoogle Scholar
  14. Dubois, D., & Prade, H. (1997a). A fuzzy set approach to case-based decision. In R. Felix (Ed.), EFDAN-97, 2nd European Workshop on Fuzzy Decision Analysis and Neural Networks for Management, Planning and Optimization (pp. 1–9). Dortmund, Germany.Google Scholar
  15. Dubois, D., & Prade, H. (1997b). The three semantics of fuzzy sets. Fuzzy Sets and Systems, 90(2), 141–150.zbMATHMathSciNetCrossRefGoogle Scholar
  16. Dubois, D., Prade, H., & Testemale, C. (1988). Weighted fuzzy pattern matching. Fuzzy Sets and Systems, 28, 313–331.zbMATHMathSciNetCrossRefGoogle Scholar
  17. Dubois, D., Fargier, H., & Prade, H. (1994). Propagation and satisfaction of flexible constraints. In R. R. Yager & L. A. Zadeh (Eds.), Fuzzy sets, neural networks and soft computing (pp. 166–187). New York: Van Nostrand Reinhold.Google Scholar
  18. Dubois, D., Fargier, H., & Prade, H. (1996a). Possibility theory in constraint satisfaction problems: Handling priority, preference and uncertainty. Applied Intelligence, 6, 287–309.CrossRefGoogle Scholar
  19. Dubois, D., Fargier, H., & Prade, H. (1996b). Refinements of the maximin approach to decisionmaking in fuzzy environment. Fuzzy Sets and Systems, 81, 103–122.zbMATHMathSciNetCrossRefGoogle Scholar
  20. Dubois, D., Esteva, F., Garcia, P., Godo, L., de Mantaras, R. L., & Prade, H. (1997). Fuzzy modelling of case-based reasoning and decision. In D. B. Leake & E. Plaza (Eds.), Case-based reasoning research and development, Proceedings ICCBR-97 (pp. 599–610). Berlin: Springer-Verlag.Google Scholar
  21. Dubois, D., Esteva, F., Garcia, P., Godo, L., Lopez de Mantaras, R., & Prade, H. (1998). Fuzzy set modelling in case-based reasoning. International Journal of Intelligent Systems, 13, 345–373.zbMATHCrossRefGoogle Scholar
  22. Dubois, D., Prade, H., & Sédes, F. (2001). Fuzzy logic techniques in multimedia database querying: A preliminary investigation of potentials. IEEE Transactions on Knowledge and Data Engineering, 13(3), 383–392.CrossRefGoogle Scholar
  23. Dubois, D., Hüllermeier, E., & Prade, H. (2002). Fuzzy set-based methods in instance-based reasoning. IEEE Transactions on Fuzzy Systems, 10(3), 322–332.CrossRefGoogle Scholar
  24. Dubois, D., Kaci, S., & Prade, H. (2004). Bipolarity in reasoning and decision: An introduction. The case of the possibility framework. In IPMU–04, 10th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Perugia, Italy.Google Scholar
  25. Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave and information tapestry. Communications of the ACM, 35(12), 61–70.CrossRefGoogle Scholar
  26. Gilboa, I., & Schmeidler, D. (1995). Case-based decision theory. Quarterly Journal of Economics, 110(4), 605–639.zbMATHCrossRefGoogle Scholar
  27. Hellman, M. E. (1970). The nearest neighbor classification rule with a reject option. IEEE Transactions on Systems, Man, and Cybernetics, SMC-6, 179–185.MathSciNetGoogle Scholar
  28. Kautz, H. (1998). Recommender systems. Menlo Park, CA: AAAI Press.Google Scholar
  29. Klement, E. P., Mesiar, R., & Pap, E. (2002). Triangular norms. Dordrecht: Kluwer Academic Publishers.zbMATHGoogle Scholar
  30. Lakoff, G. (1973). Hedges: A study in meaning criteria and the logic of fuzzy concepts. Journal of Philosophical Logic, 2, 458–508.zbMATHMathSciNetCrossRefGoogle Scholar
  31. Larsen, H., Kacprzyk, J., Zadrozny, S., Andreasen, T., & Christiansen, H. (Eds.) (2001). Flexible query answering systems, recent advances. Heidelberg: Physica Verlag.Google Scholar
  32. Lin, W., Alvarez, S. A., & Ruiz, C. (2002). Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery, 6, 83–105.MathSciNetCrossRefGoogle Scholar
  33. MacVicar-Whelan, P. J. (1978). Fuzzy sets, the concept of height, and the hedge very. IEEE Transactions on Systems, Man, and Cybernetics, 8, 507–511.Google Scholar
  34. Prade, H., & Yager, R. R. (1994). Estimations of expectedness and potential surprize in possibility theory. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2, 417–428.MathSciNetCrossRefGoogle Scholar
  35. Resnik, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3).Google Scholar
  36. Ruspini, E. H. (1991). On the semantics of fuzzy logic. International Journal of Approximate Reasoning, 5, 45–88.zbMATHMathSciNetCrossRefGoogle Scholar
  37. Yager, R. R. (1985). Aggregating evidence using quantified statements. Information Sciences, 36, 179–206.zbMATHMathSciNetCrossRefGoogle Scholar
  38. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.zbMATHMathSciNetCrossRefGoogle Scholar
  39. Zadeh, L. A. (1972). A fuzzy-set theoretic interpretation of linguistic hedges. Journal of Cybernetics, 2(3), 4–32.MathSciNetCrossRefGoogle Scholar
  40. Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1(1).Google Scholar
  41. Zadeh, L. A. (1996). Fuzzy logic = computing with words. IEEE Transactions on Fuzzy Systems, 2, 103–111.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.IRIT–Institut de Recherche en Informatique de Toulouse France
  2. 2.Department of Computer ScienceUniversity of Magdeburg Germany

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