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

Yorick Spenrath; Marwan Hassani; Boudewijn F. van Dongen; Haseeb Tariq

doi:10.1007/978-3-030-67670-4_20

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

Yorick Spenrath, Marwan Hassani, Boudewijn F. van Dongen, Haseeb Tariq

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-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 language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track - European Conference, ECML PKDD 2020, Proceedings
Editors	Yuxiao Dong, Dunja Mladenic, Craig Saunders
Publisher	Springer
Pages	323-338
Number of pages	16
ISBN (Print)	9783030676698
DOIs	https://doi.org/10.1007/978-3-030-67670-4_20
Publication status	Published - 25 Feb 2021
Event	2020 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2020) - Virtual, Online, Ghent, Belgium Duration: 14 Sep 2020 → 18 Sep 2020 https://ecmlpkdd2020.net/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12461 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2020 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2020)
Abbreviated title	ECML PKDD 2020
Country/Territory	Belgium
City	Ghent
Period	14/09/20 → 18/09/20
Internet address	https://ecmlpkdd2020.net/

Keywords

Clustering
Distance metric
Optimization
Transaction categorization

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-67670-4_20

Cite this

Spenrath, Y., Hassani, M., van Dongen, B. F., & Tariq, H. (2021). Why Did My Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data. In Y. Dong, D. Mladenic, & C. Saunders (Eds.), Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track - European Conference, ECML PKDD 2020, Proceedings (pp. 323-338). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12461 LNAI). Springer. https://doi.org/10.1007/978-3-030-67670-4_20

Spenrath, Yorick ; Hassani, Marwan ; van Dongen, Boudewijn F. et al. / Why Did My Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data. Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track - European Conference, ECML PKDD 2020, Proceedings. editor / Yuxiao Dong ; Dunja Mladenic ; Craig Saunders. Springer, 2021. pp. 323-338 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "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.",

keywords = "Clustering, Distance metric, Optimization, Transaction categorization",

author = "Yorick Spenrath and Marwan Hassani and {van Dongen}, {Boudewijn F.} and Haseeb Tariq",

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booktitle = "Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track - European Conference, ECML PKDD 2020, Proceedings",

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Spenrath, Y , Hassani, M , van Dongen, BF & Tariq, H 2021, Why Did My Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data. in Y Dong, D Mladenic & C Saunders (eds), Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track - European Conference, ECML PKDD 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12461 LNAI, Springer, pp. 323-338, 2020 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2020), Ghent, Belgium, 14/09/20. https://doi.org/10.1007/978-3-030-67670-4_20

Why Did My Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data. / Spenrath, Yorick ; Hassani, Marwan ; van Dongen, Boudewijn F. et al.

Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track - European Conference, ECML PKDD 2020, Proceedings. ed. / Yuxiao Dong; Dunja Mladenic; Craig Saunders. Springer, 2021. p. 323-338 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12461 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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 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.

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 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.

KW - Clustering

KW - Distance metric

KW - Optimization

KW - Transaction categorization

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DO - 10.1007/978-3-030-67670-4_20

M3 - Conference contribution

AN - SCOPUS:85103241217

SN - 9783030676698

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track - European Conference, ECML PKDD 2020, Proceedings

A2 - Dong, Yuxiao

A2 - Mladenic, Dunja

A2 - Saunders, Craig

PB - Springer

T2 - 2020 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2020)

Y2 - 14 September 2020 through 18 September 2020

ER -

Spenrath Y , Hassani M , van Dongen BF, Tariq H. Why Did My Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data. In Dong Y, Mladenic D, Saunders C, editors, Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track - European Conference, ECML PKDD 2020, Proceedings. Springer. 2021. p. 323-338. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-67670-4_20

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

Abstract

Publication series

Conference

Keywords

UN SDGs

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