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A Recommender System Based on Multi-Criteria Aggregation

Author

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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)

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.

Suggested Citation

  • Soumana Fomba & Pascale Zarate & Marc Kilgour & Guy Camilleri & Jacqueline Konate & Fana Tangara, 2017. "A Recommender System Based on Multi-Criteria Aggregation," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 9(4), pages 1-15, October.
    • Handle: RePEc:igg:jdsst0:v:9:y:2017:i:4:p:1-15
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