The Embeddings That Came in From the Cold: Improving Vectors for New and Rare Products with Content-Based Inference
Jacopo Tagliabue, Bingqing Yu, Federico Bianchi · 2020
Training product embeddings in a multi-tenant scenario involves solving the challenges of ever changing catalogs across dozens of deployments, without supervision. In this work, we detail how we deal with new and rare products when building neural representations at scale: we show how to inject product knowledge into behavior-based embeddings to provide the best accuracy with minimal engineering changes in existing infrastructure and without additional manual effort.