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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track - European Conference, ECML PKDD 2020, Proceedings
EditorsYuxiao Dong, Dunja Mladenic, Craig Saunders
PublisherSpringer
Pages323-338
Number of pages16
ISBN (Print)9783030676698
DOIs
Publication statusPublished - 25 Feb 2021
Event2020 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2020) - Virtual, Online, Ghent, Belgium
Duration: 14 Sep 202018 Sep 2020
https://ecmlpkdd2020.net/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12461 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2020 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2020)
Abbreviated titleECML PKDD 2020
Country/TerritoryBelgium
CityGhent
Period14/09/2018/09/20
Internet address

Keywords

  • Clustering
  • Distance metric
  • Optimization
  • Transaction categorization

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