Yuri M. Brovman

We developed a ranking model to generate personalized recommendations that optimally ranks hundreds of candidate recommendations by considering a user’s shopping experience as well as platform performance objectives.

A step-by-step guide on how to build a state-of-the-art recommender system in an industrial setting.

Generating relevant complementary item recommendations that drive conversion at eBay is a challenging problem. In this blog post, we describe some of these challenges, and how we incorporated several different signals, including behavior-based (co-purchase, co-view, co-search, popularity) and content-based (title text), to significantly enrich the number and quality of candidate recommendations. This can produce an improved user shopping experience, which can lead to increased transactions between eBay buyers and sellers, and an increase in the number of items bought, which is good for the eBay marketplace as a whole.