论文标题
从收据数据中检测多个限制的消费模式：一种非负张量分解方法
Detecting multi-timescale consumption patterns from receipt data: A non-negative tensor factorization approach
论文作者
论文摘要
了解消费者行为是一项重要的任务，不仅用于制定营销策略，而且对于管理经济政策的管理。然而，检测消费模式是一个高维的问题，其中需要考虑影响消费者行为的各种因素，例如消费者的人口统计，昼夜节律，季节性周期等。在这里，我们开发了一种从大型扫描扫描的收到的大量消费者数据集的方法来提取多次消费者的方法。我们使用非负张量分解（NTF）一次检测周内和间的消费模式。提出的方法使我们能够根据消费者的消费模式来表征，这些模式在不同的时间范围内相关。
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.
