Detecting multi-timescale consumption patterns from receipt data: a non-negative tensor factorization approach
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 20, 2020
- Source ID
- 10.1007/s42001-020-00078-5
Entities
People
- Akira Matsui
- Daisuke Moriwaki
- Emilio Ferrara
- Teruyoshi Kobayashi
Organizations
- Japan Society for the Promotion of Science