  Recommender systems are an essential component of e-commerce marketplaces,
helping consumers navigate massive amounts of inventory and find what they need
or love. In this paper, we present an approach for generating personalized item
recommendations in an e-commerce marketplace by learning to embed items and
users in the same vector space. In order to alleviate the considerable
cold-start problem present in large marketplaces, item and user embeddings are
computed using content features and multi-modal onsite user activity
respectively. Data ablation is incorporated into the offline model training
process to improve the robustness of the production system. In offline
evaluation using a dataset collected from eBay traffic, our approach was able
to improve the Recall@k metric over the Recently-Viewed-Item (RVI) method. This
approach to generating personalized recommendations has been launched to serve
production traffic, and the corresponding scalable engineering architecture is
also presented. Initial A/B test results show that compared to the current
personalized recommendation module in production, the proposed method increases
the surface rate by $\sim$6\% to generate recommendations for 90\% of listing
page impressions.
