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Article Dans Une Revue Journal of Intelligent Information Systems Année : 2006

Fuzzy methods for case-based recommendation and decision support

Résumé

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 apporach, 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 theory in some related fields.

Dates et versions

hal-03364226 , version 1 (04-10-2021)

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Didier Dubois, Eyke Hüllermeier, Henri Prade. Fuzzy methods for case-based recommendation and decision support. Journal of Intelligent Information Systems, 2006, 27, pp.95-115. ⟨10.1007/s10844-006-0976-x⟩. ⟨hal-03364226⟩
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