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

0Citations
Citations of this article
10Readers
Mendeley users who have this article in their library.

This article is free to access.

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.

Cite

CITATION STYLE

APA

Matsui, A., Kobayashi, T., Moriwaki, D., & Ferrara, E. (2023). Detecting multi-timescale consumption patterns from receipt data: a non-negative tensor factorization approach. Journal of Computational Social Science, 6(2), 1179–1192. https://doi.org/10.1007/s42001-020-00078-5

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free