Data-driven fuzzy preference analysis from an optimization perspective

Long Ren; Bin Zhu; Zeshui Xu

doi:10.1016/j.fss.2019.03.003

Data-driven fuzzy preference analysis from an optimization perspective

Long Ren, Bin Zhu^*, Zeshui Xu

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

21 Citations (Scopus)

Abstract

How to leverage massive online data to understand consumer preferences over products and services has accumulated significant attention in business. In this paper, we analyze consumer preferences by modeling it as a decision-making problem of ranking alternatives with consumers' online ratings. We propose a data-driven fuzzy preference analysis (D-FPA) method to obtain the priorities of alternatives. We show that the D-FPA is tractable and with high computation efficiency. In addition, we propose a natural indicator to measure the reliability of the derived ranking results and suggest thresholds of this indicator for better control of the method. A real-world application about online film rating is provided to illustrate the D-FPA, demonstrating that the derived ranking results converge rapidly and remain stable with the observed empirical data. Finally, we show how to build up an effective recommendation system with empirical data from MovieLens.

Original language	English
Pages (from-to)	85-101
Number of pages	17
Journal	Fuzzy Sets and Systems
Volume	377
DOIs	https://doi.org/10.1016/j.fss.2019.03.003
Publication status	Published - 15 Dec 2019

Keywords

Data-driven methods
Fuzzy preference relations
Preference analysis
Stochastic methods

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10.1016/j.fss.2019.03.003

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title = "Data-driven fuzzy preference analysis from an optimization perspective",

abstract = "How to leverage massive online data to understand consumer preferences over products and services has accumulated significant attention in business. In this paper, we analyze consumer preferences by modeling it as a decision-making problem of ranking alternatives with consumers' online ratings. We propose a data-driven fuzzy preference analysis (D-FPA) method to obtain the priorities of alternatives. We show that the D-FPA is tractable and with high computation efficiency. In addition, we propose a natural indicator to measure the reliability of the derived ranking results and suggest thresholds of this indicator for better control of the method. A real-world application about online film rating is provided to illustrate the D-FPA, demonstrating that the derived ranking results converge rapidly and remain stable with the observed empirical data. Finally, we show how to build up an effective recommendation system with empirical data from MovieLens.",

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author = "Long Ren and Bin Zhu and Zeshui Xu",

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T1 - Data-driven fuzzy preference analysis from an optimization perspective

AU - Ren, Long

AU - Zhu, Bin

AU - Xu, Zeshui

PY - 2019/12/15

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AB - How to leverage massive online data to understand consumer preferences over products and services has accumulated significant attention in business. In this paper, we analyze consumer preferences by modeling it as a decision-making problem of ranking alternatives with consumers' online ratings. We propose a data-driven fuzzy preference analysis (D-FPA) method to obtain the priorities of alternatives. We show that the D-FPA is tractable and with high computation efficiency. In addition, we propose a natural indicator to measure the reliability of the derived ranking results and suggest thresholds of this indicator for better control of the method. A real-world application about online film rating is provided to illustrate the D-FPA, demonstrating that the derived ranking results converge rapidly and remain stable with the observed empirical data. Finally, we show how to build up an effective recommendation system with empirical data from MovieLens.

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KW - Stochastic methods

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JO - Fuzzy Sets and Systems

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Data-driven fuzzy preference analysis from an optimization perspective

Abstract

Keywords

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