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Detecting multi-timescale consumption patterns from receipt data: a non-negative tensor factorization approach

Author

Listed:
  • Akira Matsui

    (University of Southern California)

  • Teruyoshi Kobayashi

    (Kobe University
    Kobe University)

  • Daisuke Moriwaki

    (AI Lab, CyberAgent, Inc.)

  • Emilio Ferrara

    (University of Southern California
    University of Southern California
    University of Southern California)

Abstract

Understanding consumer behavior is an important task, not only for developing marketing strategies but also for the management of economic policies. Detecting consumption patterns, however, is a high-dimensional problem in which various factors that would affect consumers’ behavior need to be considered, such as consumers’ demographics, circadian rhythm, seasonal cycles, etc. Here, we develop a method to extract multi-timescale expenditure patterns of consumers from a large dataset of scanned receipts. We use a non-negative tensor factorization (NTF) to detect intra- and inter-week consumption patterns at one time. The proposed method allows us to characterize consumers based on their consumption patterns that are correlated over different timescales.

Suggested Citation

  • Akira Matsui & Teruyoshi Kobayashi & Daisuke Moriwaki & Emilio Ferrara, 2023. "Detecting multi-timescale consumption patterns from receipt data: a non-negative tensor factorization approach," Journal of Computational Social Science, Springer, vol. 6(2), pages 1179-1192, October.
    • Handle: RePEc:spr:jcsosc:v:6:y:2023:i:2:d:10.1007_s42001-020-00078-5
    DOI: 10.1007/s42001-020-00078-5
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