A Recommender System Based on Multi-Criteria Aggregation

Soumana Fomba, Pascale Zarate, Marc Kilgour, Guy Camilleri, Jacqueline Konate and Fana Tangara
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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
Fana Tangara: University of Science, Technique and Technologies of Bamako, Bamako, Mali

International Journal of Decision Support System Technology (IJDSST), 2017, vol. 9, issue 4, 1-15

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jdsst0:v:9:y:2017:i:4:p:1-15

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