Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search

Ziyang Liu, Chaokun Wang, Hao Feng, Lingfei Wu, Liqun Yang

Abstract
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 Transformer-style 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.
Anthology ID:
2022.emnlp-industry.5
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
Month:
December
Year:
2022
Address:
Abu Dhabi, UAE
Editors:
Yunyao Li, Angeliki Lazaridou
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
63–76
Language:
URL:
https://aclanthology.org/2022.emnlp-industry.5
DOI:
10.18653/v1/2022.emnlp-industry.5
Bibkey:
Cite (ACL):
Ziyang Liu, Chaokun Wang, Hao Feng, Lingfei Wu, and Liqun Yang. 2022. Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 63–76, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search (Liu et al., EMNLP 2022)
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