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

Akira Matsui (), Teruyoshi Kobayashi, Daisuke Moriwaki and Emilio Ferrara ()
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Akira Matsui: University of Southern California
Emilio Ferrara: University of Southern California

Journal of Computational Social Science, 2023, vol. 6, issue 2, No 28, 1179-1192

Abstract: 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.

Keywords: Multi-timescale patterns; Consumer behavior; Consumption expenditure; Non-negative tensor factorization (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s42001-020-00078-5

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