Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search
Ziyang Liu, Chaokun Wang, Hao Feng, Lingfei Wu, Liqun Yang · 2022
Online relevance matching is an essential task of e-commerce product search to boost the utility of search engines and ensure a smooth user experience.Previous work adopts either classical relevance matching models or Transformerstyle models to address it.However, they ignore the inherent bipartite graph structures that are ubiquitous in e-commerce product search logs and are too inefficient to deploy online.In this paper, we design an efficient knowledge distillation framework for e-commerce relevance matching to integrate the respective advantages of Transformer-style models and classical relevance matching models.Especially for the core student model of the framework, we propose a novel method using k-order relevance modeling.The experimental results on large-scale real-world data (the size is 6∼174 million) show that the proposed method significantly improves the prediction accuracy in terms of human relevance judgment.We deploy our method to JD.com online search platform.The A/B testing results show that our method significantly improves most business metrics under price sort mode and default sort mode.