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Developing explicit customer preference models using fuzzy regression with nonlinear structure.

Huimin Jiang1, Xianhui Wu1, Farzad Sabetzadeh2

  • 1School of Business, Macau University of Science and Technology, Macau, China.

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|February 27, 2023
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Summary
This summary is machine-generated.

This study models consumer preferences using fuzzy regression on online reviews for smartwatches. The proposed nonlinear fuzzy regression approach effectively captures consumer preferences, aiding product design optimization.

Keywords:
Explicit consumer preference modelsFuzzy regression with nonlinear structureMulti-objective optimizationSentiment analysis

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Area of Science:

  • * E-commerce and Consumer Behavior
  • * Data Mining and Machine Learning
  • * Fuzzy Systems and Regression Analysis

Background:

  • * Online reviews provide crucial consumer feedback for product design optimization.
  • * Previous models for consumer preferences from reviews often struggle with nonlinearity and fuzzy coefficients.
  • * Explicit modeling of consumer preferences from online reviews remains a challenge.

Purpose of the Study:

  • * To develop and validate a nonlinear fuzzy regression model for understanding consumer preferences from online reviews.
  • * To explore the relationship between product attributes and consumer preferences in the smartwatch market.
  • * To offer insights for optimizing product design and enhancing consumer satisfaction.

Main Methods:

  • * Text mining of online smartwatch reviews to extract sentiment scores for product attributes.
  • * Construction of a polynomial structure linking product attributes to consumer preferences.
  • * Application of a fuzzy regression approach to determine coefficients within the nonlinear structure.
  • * Numerical comparison of the proposed method against existing techniques like fuzzy least squares and ANFIS.

Main Results:

  • * Sentiment analysis successfully identified consumer preferences related to smartwatch features.
  • * The nonlinear fuzzy regression model was established based on the polynomial structure.
  • * The proposed method demonstrated superior effectiveness in modeling consumer preferences compared to benchmark methods.
  • * Performance was evaluated using mean relative error and mean systematic confidence.

Conclusions:

  • * Nonlinear fuzzy regression is a powerful tool for modeling consumer preferences from online reviews.
  • * The developed model provides a robust framework for analyzing consumer feedback and guiding product development.
  • * This research offers valuable insights for e-commerce platforms aiming to improve product design and consumer satisfaction.