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Fuzzy methods for case-based recommendation and decision support

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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.

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Authors and Affiliations

  1. IRIT–Institut de Recherche en Informatique de Toulouse,  , France

    Didier Dubois & Henri Prade

  2. Department of Computer Science, University of Magdeburg,  , Germany

    Eyke Hüllermeier

Authors
  1. Didier Dubois
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  2. Eyke Hüllermeier
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  3. Henri Prade
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Correspondence to Eyke Hüllermeier.

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Dubois, D., Hüllermeier, E. & Prade, H. Fuzzy methods for case-based recommendation and decision support. J Intell Inf Syst 27, 95–115 (2006). https://doi.org/10.1007/s10844-006-0976-x

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  • DOI: https://doi.org/10.1007/s10844-006-0976-x

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