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

Research output: Contribution to conference › Paper › peer-review

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 language	English
Pages	165-170
Number of pages	6
Publication status	Published - 1 Jan 2018
Event	26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018) - Bruges, Bruges, Belgium Duration: 25 Apr 2018 → 27 Apr 2018

Conference

Conference	26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018)
Country/Territory	Belgium
City	Bruges
Period	25/04/18 → 27/04/18

Cite this

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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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day = "1",

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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', 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 et al.
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 conference › Paper › peer-review

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

SP - 165

EP - 170

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

Abstract

Conference

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

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

Structuring and Solving MCDM Problems using Artificial Neural Networks

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

Abstract

Conference

Other files and links

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Projects

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

Activities

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

Student theses

Structuring and Solving MCDM Problems using Artificial Neural Networks

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