A Recommender System Based on Multi-Criteria Aggregation | Zendy

Soumana Fomba | Zendy; Pascale Zaraté | Zendy; Marc Kilgour | Zendy; Guy Camilleri | Zendy; Jacqueline Konaté | Zendy; Fana Tangara | Zendy

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A Recommender System Based on Multi-Criteria Aggregation

Author(s) -

Soumana Fomba,

Pascale Zaraté,

Marc Kilgour,

Guy Camilleri,

Jacqueline Konaté,

Fana Tangara

Publication year - 2017

Publication title -

international journal of decision support system technology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.139

H-Index - 11

eISSN - 1941-630X

pISSN - 1941-6296

DOI - 10.4018/ijdsst.2017100101

Subject(s) - recommender system , computer science , decision maker , multiple criteria decision analysis , choquet integral , decision support system , order (exchange) , measure (data warehouse) , fuzzy logic , operator (biology) , data mining , operations research , information retrieval , artificial intelligence , mathematics , biochemistry , chemistry , finance , economics , repressor , transcription factor , gene

Recommender systems aim to support decision-makers by providing decision advice. We offer multi-criteria decision recommendations based on a performance matrix and a partial order on criteria submitted by the user. Our method is to aggregate performance measures over all criteria based on inferences about preferences from the decision-maker’s input. After reviewing some multicriteria aggregation operators, we present a recommender system that uses the Choquet integral of a fuzzy measure to determine a total ordering of the alternatives.

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