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Computer Science > Computation and Language

arXiv:2110.15730 (cs)
[Submitted on 13 Oct 2021]

Title:E-Commerce Dispute Resolution Prediction

Authors:David Tsurel, Michael Doron, Alexander Nus, Arnon Dagan, Ido Guy, Dafna Shahaf
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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.
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2110.15730 [cs.CL]
  (or arXiv:2110.15730v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2110.15730
Journal reference: CIKM'20: Proceedings of the 29th ACM International Conference on Information and Knowledge Management, Oct 2020, Pages 1465-1474
Related DOI: https://doi.org/10.1145/3340531.3411906

Submission history

From: Dafna Shahaf [view email]
[v1] Wed, 13 Oct 2021 09:45:06 UTC (2,758 KB)
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