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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

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.

Originele taal-2Engels
TitelMachine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track - European Conference, ECML PKDD 2020, Proceedings
RedacteurenYuxiao Dong, Dunja Mladenic, Craig Saunders
UitgeverijSpringer
Pagina's323-338
Aantal pagina's16
ISBN van geprinte versie9783030676698
DOI's
StatusGepubliceerd - 25 feb 2021
EvenementEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020 - Virtual, Online
Duur: 14 sep 202018 sep 2020

Publicatie series

NaamLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12461 LNAI
ISSN van geprinte versie0302-9743
ISSN van elektronische versie1611-3349

Congres

CongresEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020
StadVirtual, Online
Periode14/09/2018/09/20

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