Structuring and Solving Multi-Criteria Decision Making Problems using Artificial Neural Networks: A Smartphone Recommendation Case

Résultats de recherche: Contribution à un événement scientifique (non publié)Article

Résumé

Several techniques can be used to solve multi-criteria decision making (MCDM) problems and to provide a global ranking of the alternatives considered. However, in a context with a high number of alternatives and where decision criteria relate to soft goals, the decision problem is particularly hard to solve. This paper analyzes the use of artificial neural networks to improve the relevance of the ranking of alternatives delivered by MCDM problem-solving techniques. Afterwards, a model using a combination of artificial neural networks and of the weighted sum model, a particular MCDM problem-solving technique, is built to recommend smartphones.
langue originaleAnglais
Pages165-170
Nombre de pages6
étatPublié - 1 janv. 2018
Evénement 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018) - Bruges, Bruges, Belgique
Durée: 25 avr. 201827 avr. 2018

Une conférence

Une conférence 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018)
PaysBelgique
La villeBruges
période25/04/1827/04/18

Empreinte digitale

Multicriteria decision-making
Artificial neural network
Ranking
Problem solving
Decision criteria

Citer ceci

Amaral De Sousa, V., Simonofski, A., Snoeck, M., & Jureta, I. (2018). Structuring and Solving Multi-Criteria Decision Making Problems using Artificial Neural Networks: A Smartphone Recommendation Case. 165-170. Papier présenté � 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), Bruges, Belgique.
Amaral De Sousa, Victor ; Simonofski, Anthony ; Snoeck, Monique ; Jureta, Ivan. / Structuring and Solving Multi-Criteria Decision Making Problems using Artificial Neural Networks : A Smartphone Recommendation Case. Papier présenté � 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), Bruges, Belgique.6 p.
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abstract = "Several techniques can be used to solve multi-criteria decision making (MCDM) problems and to provide a global ranking of the alternatives considered. However, in a context with a high number of alternatives and where decision criteria relate to soft goals, the decision problem is particularly hard to solve. This paper analyzes the use of artificial neural networks to improve the relevance of the ranking of alternatives delivered by MCDM problem-solving techniques. Afterwards, a model using a combination of artificial neural networks and of the weighted sum model, a particular MCDM problem-solving technique, is built to recommend smartphones.",
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Amaral De Sousa, V, Simonofski, A, Snoeck, M & Jureta, I 2018, 'Structuring and Solving Multi-Criteria Decision Making Problems using Artificial Neural Networks: A Smartphone Recommendation Case', Papier présenté � 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), Bruges, Belgique, 25/04/18 - 27/04/18 p. 165-170.

Structuring and Solving Multi-Criteria Decision Making Problems using Artificial Neural Networks : A Smartphone Recommendation Case. / Amaral De Sousa, Victor; Simonofski, Anthony; Snoeck, Monique; Jureta, Ivan.

2018. 165-170 Papier présenté � 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), Bruges, Belgique.

Résultats de recherche: Contribution à un événement scientifique (non publié)Article

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AB - Several techniques can be used to solve multi-criteria decision making (MCDM) problems and to provide a global ranking of the alternatives considered. However, in a context with a high number of alternatives and where decision criteria relate to soft goals, the decision problem is particularly hard to solve. This paper analyzes the use of artificial neural networks to improve the relevance of the ranking of alternatives delivered by MCDM problem-solving techniques. Afterwards, a model using a combination of artificial neural networks and of the weighted sum model, a particular MCDM problem-solving technique, is built to recommend smartphones.

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Amaral De Sousa V, Simonofski A, Snoeck M, Jureta I. Structuring and Solving Multi-Criteria Decision Making Problems using Artificial Neural Networks: A Smartphone Recommendation Case. 2018. Papier présenté � 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), Bruges, Belgique.