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Article

Methodical Aspects of MCDM Based E-Commerce Recommender System

1
Institute of Management, University of Szczecin, ul. Cukrowa 8, 71-004 Szczecin, Poland
2
Doctoral School of University of Szczecin, ul. Mickiewicza 16, 70-383 Szczecin, Poland
3
Research Team on Intelligent Decision Support Systems, Department of Artificial Intelligence and Applied Mathematics, Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, ul. Żołnierska 49, 71-210 Szczecin, Poland
*
Author to whom correspondence should be addressed.
Academic Editors: Shib Sankar Sana, Sweety Sadhukhan and Hyunchul Ahn
J. Theor. Appl. Electron. Commer. Res. 2021, 16(6), 2192-2229; https://doi.org/10.3390/jtaer16060122
Received: 19 July 2021 / Revised: 23 August 2021 / Accepted: 30 August 2021 / Published: 2 September 2021
(This article belongs to the Special Issue The New Era of Digital Marketing)
The aim of this paper is to present the use of an innovative approach based on MCDM methods as the main component of a consumer Decision Support System (DSS) by recommending the most suitable products among a given set of alternatives. This system provides a reliable recommendation to the consumer in the form of a compromise ranking constructed from the five MCDM methods: the hybrid approach TOPSIS-COMET, COCOSO, EDAS, MAIRCA, and MABAC. Each of the methods used contributes significantly to the final compromise ranking built with the Copeland strategy. Chosen MCDM methods were combined with the objective CRITIC weighting method, and their performance was presented on the illustrative example of choosing the most suitable mobile phone. A sensitivity analysis involving the rw and WS correlation coefficients was performed to determine the match between the compromise ranking of the candidates and the rankings provided by each MCDM method. Sensitivity analysis demonstrated that all investigated compromise candidate rankings show high convergence with the rankings provided by the particular MCDM methods. Thus, the performed study proved that the proposed approach shows high potential to be successfully used as a central component of DSS for recommending the most suitable product. Such DSS could be a universal and future-proof solution for e-commerce sites and websites, providing advanced product comparison capabilities in delivering a recommendation to the user as a final ranking of alternatives. View Full-Text
Keywords: e-commerce; decision support system; MCDM; consumer multi-criteria decision-making process; compromise ranking; TOPSIS-COMET; COCOSO; EDAS; MAIRCA; MABAC e-commerce; decision support system; MCDM; consumer multi-criteria decision-making process; compromise ranking; TOPSIS-COMET; COCOSO; EDAS; MAIRCA; MABAC
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MDPI and ACS Style

Bączkiewicz, A.; Kizielewicz, B.; Shekhovtsov, A.; Wątróbski, J.; Sałabun, W. Methodical Aspects of MCDM Based E-Commerce Recommender System. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 2192-2229. https://doi.org/10.3390/jtaer16060122

AMA Style

Bączkiewicz A, Kizielewicz B, Shekhovtsov A, Wątróbski J, Sałabun W. Methodical Aspects of MCDM Based E-Commerce Recommender System. Journal of Theoretical and Applied Electronic Commerce Research. 2021; 16(6):2192-2229. https://doi.org/10.3390/jtaer16060122

Chicago/Turabian Style

Bączkiewicz, Aleksandra, Bartłomiej Kizielewicz, Andrii Shekhovtsov, Jarosław Wątróbski, and Wojciech Sałabun. 2021. "Methodical Aspects of MCDM Based E-Commerce Recommender System" Journal of Theoretical and Applied Electronic Commerce Research 16, no. 6: 2192-2229. https://doi.org/10.3390/jtaer16060122

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