E-Commerce Dispute Resolution Prediction

David Tsurel; Michael Doron; Alexander Nus; Arnon Dagan; Ido Guy; Dafna Shahaf

doi:10.1145/3340531.3411906

E-Commerce Dispute Resolution Prediction

David Tsurel, Michael Doron, Alexander Nus, Arnon Dagan, Ido Guy, Dafna Shahaf

The Rachel and Selim Benin School of Engineering and Computer Science

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

4 Scopus citations

Abstract

E-Commerce marketplaces support millions of daily transactions, and some disagreements between buyers and sellers are unavoidable. Resolving disputes in an accurate, fast, and fair manner is of great importance for maintaining a trustworthy platform. Simple cases can be automated, but intricate cases are not sufficiently addressed by hard-coded rules, and therefore most disputes are currently resolved by people. In this work we take a first step towards automatically assisting human agents in dispute resolution at scale. We construct a large dataset of disputes from the eBay online marketplace, and identify several interesting behavioral and linguistic patterns. We then train classifiers to predict dispute outcomes with high accuracy. We explore the model and the dataset, reporting interesting correlations, important features, and insights.

Original language	American English
Title of host publication	CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
Publisher	Association for Computing Machinery
Pages	1465-1474
Number of pages	10
ISBN (Electronic)	9781450368599
DOIs	https://doi.org/10.1145/3340531.3411906
State	Published - 19 Oct 2020
Event	29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland Duration: 19 Oct 2020 → 23 Oct 2020

Publication series

Name	International Conference on Information and Knowledge Management, Proceedings

Conference

Conference	29th ACM International Conference on Information and Knowledge Management, CIKM 2020
Country/Territory	Ireland
City	Virtual, Online
Period	19/10/20 → 23/10/20

Bibliographical note

Funding Information:
As mentioned, recent studies show that AI models that aid in judicial and management decision making can unwittingly inherit the biases of the humans making those decisions. Studying the biases in ODR and whether or not they manifest in this model would be instrumental to improving the decision making process and making it more fair. Acknowledgments: The authors would like to thank the reviewers for their insightful comments. This work was supported by US National Science Foundation, US-Israel Binational Science Foundation (NSF-BSF) grant 2017741 (Shahaf).

Publisher Copyright:
© 2020 ACM.

Keywords

dispute resolution
e-commerce
online transactions

Access to Document

10.1145/3340531.3411906

Cite this

Tsurel, D., Doron, M., Nus, A., Dagan, A., Guy, I., & Shahaf, D. (2020). E-Commerce Dispute Resolution Prediction. In CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management (pp. 1465-1474). (International Conference on Information and Knowledge Management, Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3340531.3411906

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author = "David Tsurel and Michael Doron and Alexander Nus and Arnon Dagan and Ido Guy and Dafna Shahaf",

note = "Funding Information: As mentioned, recent studies show that AI models that aid in judicial and management decision making can unwittingly inherit the biases of the humans making those decisions. Studying the biases in ODR and whether or not they manifest in this model would be instrumental to improving the decision making process and making it more fair. Acknowledgments: The authors would like to thank the reviewers for their insightful comments. This work was supported by US National Science Foundation, US-Israel Binational Science Foundation (NSF-BSF) grant 2017741 (Shahaf). Publisher Copyright: {\textcopyright} 2020 ACM.; 29th ACM International Conference on Information and Knowledge Management, CIKM 2020 ; Conference date: 19-10-2020 Through 23-10-2020",

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doi = "10.1145/3340531.3411906",

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Tsurel, D, Doron, M, Nus, A, Dagan, A, Guy, I & Shahaf, D 2020, E-Commerce Dispute Resolution Prediction. in CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management. International Conference on Information and Knowledge Management, Proceedings, Association for Computing Machinery, pp. 1465-1474, 29th ACM International Conference on Information and Knowledge Management, CIKM 2020, Virtual, Online, Ireland, 19/10/20. https://doi.org/10.1145/3340531.3411906

E-Commerce Dispute Resolution Prediction. / Tsurel, David; Doron, Michael; Nus, Alexander et al.
CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, 2020. p. 1465-1474 (International Conference on Information and Knowledge Management, Proceedings).

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

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N1 - Funding Information: As mentioned, recent studies show that AI models that aid in judicial and management decision making can unwittingly inherit the biases of the humans making those decisions. Studying the biases in ODR and whether or not they manifest in this model would be instrumental to improving the decision making process and making it more fair. Acknowledgments: The authors would like to thank the reviewers for their insightful comments. This work was supported by US National Science Foundation, US-Israel Binational Science Foundation (NSF-BSF) grant 2017741 (Shahaf). Publisher Copyright: © 2020 ACM.

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E-Commerce Dispute Resolution Prediction

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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