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DyCOD - Determining Cash on Delivery Limits for Real-Time E-commerce Transactions via Constrained Optimisation Modelling

Crossref DOI link: https://doi.org/10.1007/978-3-031-43427-3_4

Published Online: 2023-09-17

Published Print: 2023

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Deep, Akash https://orcid.org/0009-0007-6857-9767
Kattamuru, Sri Charan https://orcid.org/0000-0003-4986-3723
Negi, Meghana https://orcid.org/0000-0001-5211-0891
Mathew, Jose https://orcid.org/0009-0001-2578-4492
Sathyanarayana, Jairaj https://orcid.org/0009-0005-0083-8502
License Information

Text and Data Mining valid from 2023-01-01

Version of Record valid from 2023-01-01
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Chapter History

First Online: 17 September 2023

Ethical Statement

: As we propose a framework to determine the cash-on-delivery limits for e-commerce transactions, we acknowledge the ethical implications of our work. Since our work is closely associated with collecting and processing transaction level data, we assure that any data collected for the research, including any personal information, has been secured and anonymised. We commit to safeguarding and respecting the privacy of individuals’ data. We ensure that our work is agnostic to any personal information including race, gender, religion, or any other personal characteristic. We believe in equality and inclusivity as essential aspects of ethical development. We believe that our work is extensible across industries to many applications within the pay-on-delivery limit and credit limit determination domains. We emphasise that our work should not be used for any harmful purpose.

Conference Information

Conference Acronym: ECML PKDD

Conference Name: Joint European Conference on Machine Learning and Knowledge Discovery in Databases

Conference City: Turin

Conference Country: Italy

Conference Year: 2023

Conference Start Date: 18 September 2023

Conference End Date: 22 September 2023

Conference Number: 23

Conference ID: ecml2023

Conference URL: https://2023.ecmlpkdd.org/

Peer Review Information (provided by the conference organizers)

Type: Double-blind

Conference Management System: CMT

Number of Submissions Sent for Review: 829

Number of Full Papers Accepted: 196

Number of Short Papers Accepted: 0

Acceptance Rate of Full Papers: 24% - The value is computed by the equation "Number of Full Papers Accepted / Number of Submissions Sent for Review * 100" and then rounded to a whole number.

Average Number of Reviews per Paper: 3.63

Average Number of Papers per Reviewer: 4.5

External Reviewers Involved: Yes

Additional Info on Review Process: Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.

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