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

Victor Amaral De Sousa; Anthony Simonofski; Monique Snoeck; Ivan Jureta

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

Victor Amaral De Sousa, Anthony Simonofski, Monique Snoeck, Ivan Jureta

Résultats de recherche: Contribution à un événement scientifique (non publié) › Article › Revue par des pairs

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 originale	Anglais
Pages	165-170
Nombre de pages	6
Etat de la publication	Publié - 1 janv. 2018
Evénement	26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018) - Bruges, Bruges, Belgique Durée: 25 avr. 2018 → 27 avr. 2018

Une conférence

Une conférence	26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018)
Pays/Territoire	Belgique
La ville	Bruges
période	25/04/18 → 27/04/18

Autres fichiers et liens

Link to publication in Scopus

Modelling and Implementation of Blockchain-Based Solutions Supporting Cross-Organizational Business Processes
Amaral de Sousa, V., Burnay, C., Bouraga, S. & Snoeck, M.
1/09/18 → 31/08/24
Projet: Projet de thèse

26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018
Victor Amaral de Sousa (Orateur)
25 avr. 2018
Activité: Participation ou organisation d'un événement › Participation à une conférence, un congrès

Structuring and Solving MCDM Problems using Artificial Neural Networks
Auteur: Amaral De Sousa, V., 2017
Superviseur: Jureta, I. (Promoteur), Snoeck, M. (Promoteur) & Simonofski, A. (Copromoteur)
Student thesis: Master types › Master en ingénieur de gestion à finalité spécialisée en Analytics & Digital Business

Contient cette citation

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

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. ",

author = "{Amaral De Sousa}, Victor and Anthony Simonofski and Monique Snoeck and Ivan Jureta",

year = "2018",

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language = "English",

pages = "165--170",

note = " 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018) ; Conference date: 25-04-2018 Through 27-04-2018",

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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 et al.
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 › Revue par des pairs

TY - CONF

T1 - Structuring and Solving Multi-Criteria Decision Making Problems using Artificial Neural Networks

T2 - 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018)

AU - Amaral De Sousa, Victor

AU - Simonofski, Anthony

AU - Snoeck, Monique

AU - Jureta, Ivan

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

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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M3 - Paper

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Y2 - 25 April 2018 through 27 April 2018

ER -

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

Résumé

Une conférence

Autres fichiers et liens

Empreinte digitale

Projets

Modelling and Implementation of Blockchain-Based Solutions Supporting Cross-Organizational Business Processes

Activités

26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018

Thèses de l'étudiant

Structuring and Solving MCDM Problems using Artificial Neural Networks

Contient cette citation