@inproceedings{liu-etal-2022-knowledge,
title = "Knowledge Distillation based Contextual Relevance Matching for {E}-commerce Product Search",
author = "Liu, Ziyang and
Wang, Chaokun and
Feng, Hao and
Wu, Lingfei and
Yang, Liqun",
editor = "Li, Yunyao and
Lazaridou, Angeliki",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-industry.5",
doi = "10.18653/v1/2022.emnlp-industry.5",
pages = "63--76",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search
%A Liu, Ziyang
%A Wang, Chaokun
%A Feng, Hao
%A Wu, Lingfei
%A Yang, Liqun
%Y Li, Yunyao
%Y Lazaridou, Angeliki
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F liu-etal-2022-knowledge
%X 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.
%R 10.18653/v1/2022.emnlp-industry.5
%U https://aclanthology.org/2022.emnlp-industry.5
%U https://doi.org/10.18653/v1/2022.emnlp-industry.5
%P 63-76
Markdown (Informal)
[Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search](https://aclanthology.org/2022.emnlp-industry.5) (Liu et al., EMNLP 2022)
ACL