A Learning Web Platform Based on a Fuzzy Linguistic Recommender System to Help Students to Learn Recommendation Techniques

Author(s):  
Carlos Porcel ◽  
Maria Jesús Lizarte ◽  
Juan Bernabé-Moreno ◽  
Enrique Herrera-Viedma
Author(s):  
Soumana Fomba ◽  
Pascale Zarate ◽  
Marc Kilgour ◽  
Guy Camilleri ◽  
Jacqueline Konate ◽  
...  

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.


2009 ◽  
Vol 36 (3) ◽  
pp. 5173-5183 ◽  
Author(s):  
C. Porcel ◽  
A.G. López-Herrera ◽  
E. Herrera-Viedma

2019 ◽  
Vol 162 ◽  
pp. 916-923
Author(s):  
Álvaro Tejeda-Lorente ◽  
Juan Bernabé-Moreno ◽  
Julio Herce-Zelaya ◽  
Carlos Porcel ◽  
Enrique Herrera-Viedma

2014 ◽  
Vol 31 ◽  
pp. 1036-1043 ◽  
Author(s):  
A. Tejeda-Lorente ◽  
J. Bernabé-Moreno ◽  
C. Porcel ◽  
E. Herrera-Viedma

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