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A multiple criteria Bayesian hierarchical model for analyzing heterogeneous consumer preferences


[ 1 ] Center for Intelligent Decision-making and Machine Learning, School of Management, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, PR China | [ 2 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ 3 ] School of Economics and Management, Xi’an University of Technology, Xi’an, 710048, Shaanxi, PR China | [ 4 ] School of Software Engineering, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, PR China | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication


Published in


Journal year: 2024 | Journal volume: vol. 128

Article type

scientific article

Publication language


  • Multiple criteria decision aiding
  • Bayesian hierarchical model
  • Consumer preference analysis
  • Market segmentation
  • Preference heterogeneity
  • Preference sparsity

EN We introduce a novel Bayesian hierarchical model for consumer preference analysis, addressing two significant challenges in this domain. First, it accommodates preference heterogeneity at both individual and segment levels. This enables actionable insights for targeting and pricing decisions while quantifying uncertainty. Second, it incorporates probabilistic value-based ranking to handle inconsistent and sparse preference data. This way, it mitigates the impact of cognitive biases and alleviates uncertainty in estimates. The proposed method performs robust inference of consumers’ preferences through hierarchical priors, allowing for flexible parameter learning and borrowing statistical strength from well-informed individuals. We demonstrate its practical usefulness by analyzing the real preferences of almost one hundred consumers considering mobile phone contracts. We also report the results of an extensive experimental study. The proposed method outperforms its counterpart, executing an independent estimation and the state-of-the-art approaches regarding predictive accuracy and preference similarity within identified customer groups. The performance improvements are more pronounced with larger sample sizes, smaller sets of items, and in contexts with reduced heterogeneity and increased consistency among consumers.

Date of online publication


Pages (from - to)

103113-1 - 103113-19




Ministry points / journal


Impact Factor

6,7 [List 2023]

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