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


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.


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


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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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