A Recommender System Based on Multi-Criteria Aggregation

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...

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Bibliographic Details
Published in:International journal of decision support system technology Vol. 9; no. 4; pp. 1 - 15
Main Authors: Fomba, Soumana, Zarate, Pascale, Kilgour, Marc, Camilleri, Guy, Konate, Jacqueline, Tangara, Fana
Format: Journal Article
Language:English
Published: Hershey IGI Global 01.10.2017
Subjects:
Analysis
Artificial Intelligence
Collaboration
Computation and Language
Computer Science
Decision analysis
Decision making
Decision support systems
Logic in Computer Science
Machine Learning
Multiple criterion
Operators (mathematics)
Preferences
Recommender systems
France
Recommender system
Choquet integral – MCDA
ISSN:1941-6296, 1941-630X
Online Access:Get full text
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Summary: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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ISSN:1941-6296
1941-630X
DOI:10.4018/IJDSST.2017100101