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 $r_w$ 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.

中文翻译：

---

基于 MCDM 的电子商务推荐系统的方法方面

本文的目的是通过推荐一组给定的替代品中最合适的产品，介绍使用基于 MCDM 方法的创新方法作为消费者决策支持系统 (DSS) 的主要组成部分。该系统以由五种 MCDM 方法构建的折衷排名的形式向消费者提供可靠的推荐：混合方法 TOPSIS-COMET、COCOSO、EDAS、MAIRCA 和 MABAC。所使用的每种方法都对使用 Copeland 策略构建的最终折衷排名做出了重大贡献。选定的 MCDM 方法与客观 CRITIC 加权方法相结合，并在选择最合适的手机的说明性示例中展示了它们的性能。敏感性分析涉及$r_{瓦}$ 和 $宽秒$执行相关系数以确定候选者的折衷排名与每个 MCDM 方法提供的排名之间的匹配。敏感性分析表明，所有调查的折衷候选排名都显示出与特定 MCDM 方法提供的排名的高度收敛性。因此，所进行的研究证明，所提出的方法显示出被成功用作 DSS 的核心组件以推荐最合适产品的巨大潜力。这种 DSS 可以成为电子商务网站和网站的通用且面向未来的解决方案，提供先进的产品比较功能，向用户提供推荐，作为备选方案的最终排名。