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A Recommender System Based on Multi-Criteria Aggregation
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Author(s): Soumana Fomba (University of Science, Technique and Technologies of Bamako, Bamako, Mali & University of Toulouse, Toulouse, France), Pascale Zarate (University of Toulouse, Toulouse, France), Marc Kilgour (Wilfrid Laurier University, Waterloo, Canada), Guy Camilleri (University of Toulouse, Toulouse, France), Jacqueline Konate (University of Science, Technique and Technologies of Bamako, Bamako, Mali)and Fana Tangara (University of Science, Technique and Technologies of Bamako, Bamako, Mali)
Copyright: 2017
Volume: 9
Issue: 4
Pages: 15
Source title:
International Journal of Decision Support System Technology (IJDSST)
DOI: 10.4018/IJDSST.2017100101
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Abstract
Recommender systems aim to support decision-makers by providing decision advice. We review briefly tools of Multi-Criteria Decision Analysis (MCDA), including aggregation operators, that could be the basis for a recommender system. Then we develop a multi-criteria recommender system, STROMa (SysTem of RecOmmendation Multi-criteria), to support decisions by aggregating measures of performance contained in a performance matrix. The system makes inferences about preferences using a partial order on criteria input by the decision-maker. To determine a total ordering of the alternatives, STROMa uses a multi-criteria aggregation operator, the Choquet integral of a fuzzy measure. Thus, recommendations are calculated using partial preferences provided by the decision maker and updated by the system. An integrated web platform is under development.
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