Optimizing similar item recommendations in a semi-structured marketplace to maximize conversion

Abstract

This paper tackles the problem of recommendations in eBay's large semi-structured marketplace. eBay's variable inven- tory and lack of structured information about listings makes traditional collaborative filtering algorithms difficult to use. We discuss how to overcome these data limitations to pro- duce high quality recommendations in real time with a com- bination of a customized scalable architecture as well as a widely applicable machine learned ranking model. A point- wise ranking approach is utilized to reduce the ranking prob- lem to a binary classification problem optimized on past user purchase behavior. We present details of a sampling strategy and feature engineering that have been critical to achieve a lift in both purchase through rate (PTR) and revenue.