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

Research output: Contribution to conferencePaper

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.
Original languageEnglish
Pages165-170
Number of pages6
Publication statusPublished - 1 Jan 2018
Event 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018) - Bruges, Bruges, Belgium
Duration: 25 Apr 201827 Apr 2018

Conference

Conference 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018)
CountryBelgium
CityBruges
Period25/04/1827/04/18

Fingerprint

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

Cite this

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. Paper presented at 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), Bruges, Belgium.
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. Paper presented at 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), Bruges, Belgium.6 p.
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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' Paper presented at 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), Bruges, Belgium, 25/04/18 - 27/04/18, pp. 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 Paper presented at 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), Bruges, Belgium.

Research output: Contribution to conferencePaper

TY - CONF

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

T2 - A Smartphone Recommendation Case

AU - Amaral De Sousa, Victor

AU - Simonofski, Anthony

AU - Snoeck, Monique

AU - Jureta, Ivan

PY - 2018/1/1

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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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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. Paper presented at 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), Bruges, Belgium.