Reputation and feedback systems in online marketplaces are often biased, making it difficult to ascertain the quality of sellers. We use post-transaction, buyer-to-seller message traffic to detect signals of unsatisfactory transactions on eBay. We posit that a message sent after the item was paid for serves as a reliable indicator that the buyer may be unhappy with that purchase, particularly when the message included words associated with a negative experience. The fraction of a seller's message traffic that was negative predicts whether a buyer who transacts with this seller will stop purchasing on eBay, implying that platforms can use these messages as an additional signal of seller quality.
Leveraging the power of data and economic engineering to understand and advance e-commerce.
In proceedings of the Workshop on Log-based Personalization (the 4th WSCD workshop) at WSDM 2014
A Large Scale Query Logs Analysis for Assessing Personalization Opportunities in E-commerce Sites
Neel Sundaresan, Zitao Liu
Personalization offers the promise of improving online search and shopping experience. In this work, we perform a large scale analysis on the sample of eBay query logs, which involves 9.24 billion session data spanning 12 months (08/2012-07/2013) and address the following topics
(1) What user information is useful for personalization;
(2) Importance of per-query personalization
(3) Importance of recency in query prediction.
In this paper, we study these problems and provide some preliminary conclusions