Fuzzy methods for case-based recommendation and decision support |
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Authors: | Didier Dubois Eyke Hüllermeier Henri Prade |
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Affiliation: | (1) IRIT–Institut de Recherche en Informatique de Toulouse, , France;(2) Department of Computer Science, University of Magdeburg, , Germany |
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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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Keywords: | Case-based reasoning Recommender systems Fuzzy sets Approximate reasoning Decision making Nearest neighbor estimation |
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