A Recommender System Based on Multi-Criteria Aggregation

A Recommender System Based on Multi-Criteria Aggregation

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 |Pages: 15
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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Recommendation Systems

Recommendation systems are as interactive decision support systems to take into account evolving preferences of users with a view to make recommendations. There are three main families of recommendation systems:

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