Personalized Embedding-based e-Commerce Recommendations at eBay

• URL: http://arxiv.org/abs/2102.06156v1
• Date: Thu, 11 Feb 2021 17:58:51 GMT
• Title: Personalized Embedding-based e-Commerce Recommendations at eBay
• Authors: Tian Wang, Yuri M. Brovman, Sriganesh Madhvanath
• Abstract summary: 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. Data ablation is incorporated into the offline model training process to improve the robustness of the production system.
• Score: 3.1236273633321416
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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