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 ∼6\% to generate recommendations for 90\% of listing page impressions.
@article{arxiv.2102.06156,
title = {Personalized Embedding-based e-Commerce Recommendations at eBay},
author = {Tian Wang and Yuri M. Brovman and Sriganesh Madhvanath},
journal= {arXiv preprint arXiv:2102.06156},
year = {2021}
}