E-Commerce Dispute Resolution Prediction
This work addresses the challenge of scaling dispute resolution for e-commerce platforms, but it is incremental as it focuses on assisting human agents rather than full automation.
The paper tackled the problem of automating dispute resolution in e-commerce by constructing a large dataset from eBay and training classifiers to predict outcomes with high accuracy.
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.