TY - GEN
T1 - Why did my Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data
AU - Spenrath, Yorick
AU - Hassani, Marwan
AU - van Dongen, Boudewijn F.
AU - Tariq, Haseeb
PY - 2021/2/25
Y1 - 2021/2/25
N2 - 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.
AB - 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.
UR - https://link.springer.com/chapter/10.1007%2F978-3-030-67670-4_20
M3 - Conference contribution
BT - Proceedings of ECML PKDD 2020
ER -