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 specificc information, such as extensive product hierarchies or a large product catalog. 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 that we can identify clusters of consumer behaviour.
|Title of host publication||Proceedings of ECML PKDD 2020 Lecture Notes in Computer Science Springer|
|Publication status||Accepted/In press - 2020|
Spenrath, Y., Hassani, M., van Dongen, B. F., & Tariq, H. (Accepted/In press). Why did my Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data. In Proceedings of ECML PKDD 2020 Lecture Notes in Computer Science Springer