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

• URL: http://arxiv.org/abs/2004.13277v2
• Date: Sun, 2 Aug 2020 04:03:23 GMT
• Title: Detecting multi-timescale consumption patterns from receipt data: A non-negative tensor factorization approach
• Authors: Akira Matsui, Teruyoshi Kobayashi, Daisuke Moriwaki, Emilio Ferrara
• Abstract summary: 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.
• Score: 6.550253537991014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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