Retrieval-Enhanced Dual Encoder Training for Product Matching
Justin Chiu · 2023
Product matching is the task of matching a seller-listed item to an appropriate product.It is a critical task for an e-commerce platform, and the approach needs to be efficient to run in a large-scale setting.A dual encoder approach has been a common practice for product matching recently, due to its high performance and computation efficiency.In this paper, we propose a two-stage training for the dual encoder model.Stage 1 trained a dual encoder to identify the more informative training data.Stage 2 then train on the more informative data to get a better dual encoder model.This technique is a learned approach for building training data.We evaluate the retrieval-enhanced training on two different datasets: a publicly available Large-Scale Product Matching dataset and a real-world e-commerce dataset containing 47 million products.Experiment results show that our approach improved by 2% F1 on the public dataset and 9% F1 on the real-world e-commerce dataset.