Application of Preference Selection Index and TOPSIS in Product Aspect Extraction and Ranking

Saif Addeen Alrababah, Nawaf O. Alsrehin
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Abstract

: Decision-making methodologies can differentiate between several types of criterion weights. The subjective weights of decision-makers are prone to be influenced by various factors, including their level of knowledge, experience and competency. This may result in the wrong evaluation of the criteria due to the inherent ambiguity of human judgments, leading to unavoidable assessment errors. Beyond that, while assessing the decision alternatives, the majority of Multiple Criteria Decision Making (MCDM) take the evaluation criteria into consideration separately. However, in actual application, most of the criteria are not mutually exclusive. In the context of online customer reviews, it is essential to prioritize product aspects in order to facilitate the purchasing process for potential consumers. Selecting the appropriate product aspects is a difficult task due to the vast quantity of product reviews. This research develops an MCDM solution through the integration of the Preference Selection Index (PSI) with The approach for Order Preference by Similarity to an Ideal Solution (TOPSIS) method for decision-making. The contribution of this study is to enhance the TOPSIS ranking technique by incorporating PSI objective weights as an alternative to subjective weights. PSI offers the benefit of focusing on the convergence of the criteria involved rather than their divergence. This approach will improve the ranking process of TOPSIS by taking into account the interconnectedness of the criteria, hence facilitating the prioritization of significant aspects of a product based on online reviews. A dataset comprising four electronic products was utilized as a reference for conducting a statistical analysis. Through the examination of the outcomes utilizing the discount cumulative gain metric, it becomes apparent that the combination of the TOPSIS approach alongside PSI weights facilitates the identification of the suitable product aspects that effectively differentiate the one that aligns with consumer expectations.
偏好选择指数和 TOPSIS 在产品特征提取和排序中的应用
:决策方法可以区分几种类型的标准权重。决策者的主观权重容易受到各种因素的影响,包括他们的知识水平、经验和能力。由于人类判断本身的模糊性,这可能会导致对标准的错误评估,从而造成不可避免的评估错误。除此之外,在评估决策备选方案时,大多数多标准决策(MCDM)都会将评估标准分开考虑。然而,在实际应用中,大多数标准并不是相互排斥的。就在线客户评论而言,必须对产品的各个方面进行优先排序,以促进潜在消费者的购买过程。由于产品评论数量庞大,选择合适的产品方面是一项艰巨的任务。本研究通过将偏好选择指数(PSI)与理想解相似度排序偏好法(TOPSIS)相结合,开发了一种用于决策的 MCDM 解决方案。本研究的贡献在于通过纳入 PSI 客观权重来替代主观权重,从而增强了 TOPSIS 排序技术。PSI 的优势在于关注相关标准的趋同而非分歧。这种方法考虑到了标准之间的相互联系,从而改进了 TOPSIS 的排序过程,有利于根据在线评论确定产品重要方面的优先次序。在进行统计分析时,参考了由四种电子产品组成的数据集。通过使用折现累积收益指标对结果进行检验,可以明显看出,TOPSIS 方法与 PSI 权重相结合,有助于确定合适的产品方面,从而有效区分出符合消费者期望的产品。
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来源期刊
Journal of Computer Science
Journal of Computer Science Computer Science-Computer Networks and Communications
CiteScore
1.70
自引率
0.00%
发文量
92
期刊介绍: Journal of Computer Science is aimed to publish research articles on theoretical foundations of information and computation, and of practical techniques for their implementation and application in computer systems. JCS updated twelve times a year and is a peer reviewed journal covers the latest and most compelling research of the time.
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