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Comparing unsupervised probabilistic machine learning methods for market basket analysis

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  • Harald Hruschka

    (University of Regensburg)

Abstract

We compare several unsupervised probabilistic machine learning methods for market basket analysis, namely binary factor analysis, two topic models (latent Dirichlet allocation and the correlated topic model), the restricted Boltzmann machine and the deep belief net. After an overview of previous applications of unsupervised probabilistic machine learning methods to market basket analysis we shortly present the methods which we investigate and outline their estimation. Performance is measured by tenfold cross-validated log likelihood values. Binary factor analysis vastly outperforms topic models. The restricted Boltzmann machine attains a similar performance advantage over binary factor analysis. Overall, a deep belief net with 45 variables in the first and 15 variables in the second hidden layers turns out to be the best model. We also compare the investigated machine learning methods with respect to ease of interpretation and runtimes. In addition, we show how to interpret the relationships between hidden variables and observed category purchases. To demonstrate managerial implications we estimate the effect of promoting each category both on purchase probability increases of other product categories and the relative increase of basket size. Finally, we indicate several possibilities to extend restricted Boltzmann machines and deep belief nets for market basket analysis.

Suggested Citation

  • Harald Hruschka, 2021. "Comparing unsupervised probabilistic machine learning methods for market basket analysis," Review of Managerial Science, Springer, vol. 15(2), pages 497-527, February.
    • Handle: RePEc:spr:rvmgts:v:15:y:2021:i:2:d:10.1007_s11846-019-00349-0
    DOI: 10.1007/s11846-019-00349-0
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    References listed on IDEAS

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    1. Boztug, Yasemin & Reutterer, Thomas, 2008. "A combined approach for segment-specific market basket analysis," European Journal of Operational Research, Elsevier, vol. 187(1), pages 294-312, May.
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    5. Li Cai, 2010. "High-dimensional Exploratory Item Factor Analysis by A Metropolis–Hastings Robbins–Monro Algorithm," Psychometrika, Springer;The Psychometric Society, vol. 75(1), pages 33-57, March.
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    Cited by:

    1. Mariflor Vega Carrasco & Ioanna Manolopoulou & Jason O'Sullivan & Rosie Prior & Mirco Musolesi, 2022. "Posterior summaries of grocery retail topic models: Evaluation, interpretability and credibility," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 562-588, June.
    2. Harald Hruschka, 2022. "Analyzing joint brand purchases by conditional restricted Boltzmann machines," Review of Managerial Science, Springer, vol. 16(4), pages 1117-1145, May.
    3. Andreas Falke & Harald Hruschka, 2022. "Analyzing browsing across websites by machine learning methods," Journal of Business Economics, Springer, vol. 92(5), pages 829-852, July.

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    More about this item

    Keywords

    Machine learning; Market basket analysis; Factor analysis; Topic models; Restricted Boltzmann machine; Deep learning;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other

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