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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

4 Citaten (Scopus)

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-2	Engels
Titel	Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track - European Conference, ECML PKDD 2020, Proceedings
Redacteuren	Yuxiao Dong, Dunja Mladenic, Craig Saunders
Uitgeverij	Springer
Pagina's	323-338
Aantal pagina's	16
ISBN van geprinte versie	9783030676698
DOI's	https://doi.org/10.1007/978-3-030-67670-4_20
Status	Gepubliceerd - 25 feb. 2021
Evenement	2020 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2020) - Virtual, Online, Ghent, België Duur: 14 sep. 2020 → 18 sep. 2020 https://ecmlpkdd2020.net/

Publicatie series

Naam	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12461 LNAI
ISSN van geprinte versie	0302-9743
ISSN van elektronische versie	1611-3349

Congres

Congres	2020 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2020)
Verkorte titel	ECML PKDD 2020
Land/Regio	België
Stad	Ghent
Periode	14/09/20 → 18/09/20
Internet adres	https://ecmlpkdd2020.net/

Duurzame ontwikkelingsdoelstellingen van de VN

Deze output draagt bij aan de volgende duurzame ontwikkelingsdoelstelling(en)

Toegang tot document

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

Andere bestanden en links

Link naar publicatie in Scopus

Citeer dit

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

@inproceedings{a2f6515a56724e2cb9054496727413d7,

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",

year = "2021",

month = feb,

day = "25",

doi = "10.1007/978-3-030-67670-4_20",

language = "English",

isbn = "9783030676698",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "323--338",

editor = "Yuxiao Dong and Dunja Mladenic and Craig Saunders",

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

address = "Germany",

note = "2020 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2020), ECML PKDD 2020 ; Conference date: 14-09-2020 Through 18-09-2020",

url = "https://ecmlpkdd2020.net/",

}

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 (uitgave), 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, blz. 323-338, 2020 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2020), Ghent, België, 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. uitgave / Yuxiao Dong; Dunja Mladenic; Craig Saunders. Springer, 2021. blz. 323-338 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12461 LNAI).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › 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

UR - http://www.scopus.com/inward/record.url?scp=85103241217&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-67670-4_20

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)

SP - 323

EP - 338

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, redacteurs, Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track - European Conference, ECML PKDD 2020, Proceedings. Springer. 2021. blz. 323-338. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-67670-4_20

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

Samenvatting

Publicatie series

Congres

Duurzame ontwikkelingsdoelstellingen van de VN

Toegang tot document

Andere bestanden en links

Vingerafdruk

Citeer dit