A Recommender System Based on Multi-Criteria Aggregation

A Recommender System Based on Multi-Criteria Aggregation

Soumana Fomba, Pascale Zarate, Marc Kilgour, Guy Camilleri, Jacqueline Konate, Fana Tangara
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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