David Tsurel

David Tsurel, Michael Doron, Alexander Nus et al.

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.

David Tsurel, Dan Pelleg, Ido Guy et al.

A significant portion of web search queries directly refers to named entities. Search engines explore various ways to improve the user experience for such queries. We suggest augmenting search results with {\em trivia facts} about the searched entity. Trivia is widely played throughout the world, and was shown to increase users' engagement and retention. Most random facts are not suitable for the trivia section. There is skill (and art) to curating good trivia. In this paper, we formalize a notion of \emph{trivia-worthiness} and propose an algorithm that automatically mines trivia facts from Wikipedia. We take advantage of Wikipedia's category structure, and rank an entity's categories by their trivia-quality. Our algorithm is capable of finding interesting facts, such as Obama's Grammy or Elvis' stint as a tank gunner. In user studies, our algorithm captures the intuitive notion of "good trivia" 45\% higher than prior work. Search-page tests show a 22\% decrease in bounce rates and a 12\% increase in dwell time, proving our facts hold users' attention.