Why Did My Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data
Abstract
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.
Cite
Text
Spenrath et al. "Why Did My Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020. doi:10.1007/978-3-030-67670-4_20Markdown
[Spenrath et al. "Why Did My Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020.](https://mlanthology.org/ecmlpkdd/2020/spenrath2020ecmlpkdd-my/) doi:10.1007/978-3-030-67670-4_20BibTeX
@inproceedings{spenrath2020ecmlpkdd-my,
title = {{Why Did My Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data}},
author = {Spenrath, Yorick and Hassani, Marwan and van Dongen, Boudewijn F. and Tariq, Haseeb},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2020},
pages = {323-338},
doi = {10.1007/978-3-030-67670-4_20},
url = {https://mlanthology.org/ecmlpkdd/2020/spenrath2020ecmlpkdd-my/}
}