Personalized Product Evaluation Based on GRA-TOPSIS and Kansei Engineering

Abstract

With the improvement of human living standards, users’ requirements have changed from function to emotion. Helping users pick out the most suitable product based on their subjective requirements is of great importance for enterprises. This paper proposes a Kansei engineering-based grey relational analysis and techniques for order preference by similarity to ideal solution (KE-GAR-TOPSIS) method to make a subjective user personalized ranking of alternative products. The KE-GRA-TOPSIS method integrates five methods, including Kansei Engineering (KE), analytic hierarchy process (AHP), entropy, game theory, and grey relational analysis-TOPSIS (GRA-TOPSIS). First, an evaluation system is established by KE and AHP. Second, we define a matrix variate—Kansei decision matrix (KDM)—to describe the satisfaction of user requirements. Third, the AHP is used to obtain subjective weight. Next, the entropy method is employed to obtain objective weights by taking the KDM as input. Then the two types of weights are optimized using game theory to obtain the comprehensive weights. Finally, the GRA-TOPSIS method takes the comprehensive weights and the KMD as inputs to rank alternatives. A comparison of the KE-GRA-TOPSIS, KE-TOPSIS, KE-GRA, GRA-TOPSIS, and TOPSIS is conducted to illustrate the unique merits of the KE-GRA-TOPSIS method in Kansei evaluation. Finally, taking the electric drill as an example, we describe the process of the proposed method in detail, which achieves a symmetry between the objectivity of products and subjectivity of users.

Hybrid MCDM Model for Location of Logistics Hub: A Case in China Under the Belt and Road Initiative

“…TODIM [40] The evasion of the losses and risks of the DMs can be reflected. Furthermore, determining the weights is critical to rank the alternatives, and both subjective and objective weights must be considered [41]. Therefore, using combinative weighting, which ensures a balance between the subjective and objective aspects, is more suitable to determine the evaluation criteria for the location decision [42].…”

Evaluation and Balance of Cognitive Friction: Evaluation of Product Target Image Form Combining Entropy and Game Theory

“…Commonly used methods include the semantic difference (SD) method [23], physiological signal experiment method [24], natural language processing [25], and factor analysis [26]. Then, to establish the correlation between perceptual evaluation and design elements, common methods include quantitative theory Ⅰ [27], rough set theory [28], optimal ideal solution ranking [29], and support vector machines [30]. Finally, researchers use intelligent algorithms to train and optimize models to guide subsequent design.…”

“…In the study of evaluation problems, only a single subjective or objective weighting method cannot accurately express the relationship between the evaluation subjects and the true situation of the evaluated information, which is likely to lead to a lack of information and affect the evaluation results [40]. Based on game theory, the problem of information loss caused by a single weighting can be reduced to a large extent, and accurate evaluation results can be obtained through the combined weighting of the evaluation process of cognitive subjects [29]. The design and use of the product are the processes of encoding and decoding.…”

A User Biology Preference Prediction Model Based on the Perceptual Evaluations of Designers for Biologically Inspired Design

“…Although the user preference can be represented in a perceptual space where each Kansei word has an independent dimension, it is extremely complex to determine the relationship between the designer perception and the user preference in such a 21-dimensional semantic space. Moreover, there may be potential correlations between the semantic items [55][56][57]. Hence, in this study, a exploratory factor analysis (EFA) of the questionnaire results presented in Figure 6 was conducted to probe these potential correlations and examine the factor structure of the 21-item instrument.…”