Why Did My Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data

• Published in: Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track Vol. 12461; pp. 323 - 338
• Main Authors: Spenrath, Yorick, Hassani, Marwan, Dongen, Boudewijn van, Tariq, Haseeb
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Clustering; Distance metric; Optimization; Transaction categorization
• ISBN: 9783030676698; 3030676692
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-67670-4_20

Transaction analysis is an important part in studies aiming to understand consumer behaviour. The first step is defining a proper measure of similarity, or more specifically a distance metric, between transactions. Existing distance metrics on transactional data are built on retailer specific information, such as extensive product hierarchies or a large product catalogue. In this paper we propose a new distance metric that is retailer independent by design, allowing cross-retailer and cross-country analysis. The metric comes with a novel method of finding the importance of categories of products, alternating between unsupervised learning techniques and importance calibration. We test our methodology on a real-world dataset and show how we can identify clusters of consumer behaviour.