Methodical Aspects of MCDM Based E-Commerce Recommender System

Abstract

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.

Optimal Design of Wood/Rice Husk-Waste-Filled PLA Biocomposites Using Integrated CRITIC–MABAC-Based Decision-Making Algorithm

“…In addition to being a simple, popular and organized computational Pamučar and Ćirović [50] proposed the MABAC approach, a newly designed MADM method. Building a border approximation area matrix and evaluating its distance from each alternative is one of the MABAC method's main principles [40,41,44]. The various advantages of the MABAC approach are its easy computational procedure, consistency in the computed results and use of both the profit and loss values of attributes in the computational process.…”

“…MADM approaches are the systematic ways for determining the importance of attributes (material properties) by examining a specific application and selecting the best candidate material, while eliminating the inappropriate alternatives [35,36]. Various MADM approaches, such as AHP, GRA, CRITIC, TOPSIS, VIKOR, SAW, MEW, COPRAS, PSI and ELECTRE, etc., have been created to assist in rating material alternatives and picking the best one [37][38][39][40][41][42][43][44]. Among them, MABAC and CRITIC are two popular ones.…”

Optimal Design of Ceramic Based Hip Implant Composites Using Hybrid AHP-MOORA Approach

“…These are statistical methods used to tackle complex decision-making problems utilising a finite number of qualities and options [ 31 ]. MCDM approaches include AHP (analytic hierarchy process), MOORA (multiple objective optimisation on the basis of ratio analysis), TOPSIS (a technique for the ordering of preference by similarity to ideal solution), VIKOR (vise kriterijumska optimizacija kompromisno resenje), MABAC (multi-attributive border approximation area comparison), SAW (simple additive weighting), MEW (multiplicative exponent weighting), and COPRAS (complex proportional assessment) have been effectively deployed to tackle a variety of decision-making problems [ 32 , 33 , 34 , 35 , 36 , 37 ]. Among them, MOORA and AHP are very popular.…”

Ensemble Feature Selection in Binary Machine Learning Classification: A Novel Application of the Evaluation Based on Distance from Average Solution (EDAS) Method

“…Aside from selecting the best option, MCDM also offers approaches for ranking the alternatives subject to numerous conflicting criteria. For example, Ba ˛czkiewicz et al [11] developed an e-commerce recommender system based on MCDM that ranks products based on their comparisons.…”