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

arXiv: https://arxiv.org/abs/2210.01701 · HTML: https://mlsanity.com/paper/2210.01701/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2022.

## Built on by (most cited first, 7 of 7)

- [We Should Evaluate Real-World Impact](https://mlsanity.com/paper/2507.05973/) (2025)
- [LREF: A Novel LLM-based Relevance Framework for E-commerce](https://mlsanity.com/paper/2503.09223/) (2025)
- [From Reasoning LLMs to BERT: A Two-Stage Distillation Framework for Search Relevance](https://mlsanity.com/paper/2510.11056/) (2025)
- [Knowledge Distillation for Enhancing Walmart E-commerce Search Relevance Using Large Language Models](https://mlsanity.com/paper/2505.07105/) (2025)
- [LORE: A Large Generative Model for Search Relevance](https://mlsanity.com/paper/2512.03025/) (2025)
- [Beyond Retrieval-Ranking: A Multi-Agent Cognitive Decision Framework for E-Commerce Search](https://mlsanity.com/paper/2510.20567/) (2025)
- [RAIR: A Rule-Aware Benchmark Uniting Challenging Long-Tail and Visual Salience Subset for E-commerce Relevance Assessment](https://mlsanity.com/paper/2512.24943/) (2025)

## Abstract (arXiv)

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 the anonymous online search platform. The A/B testing results show that our method significantly improves 5.7% of UV-value under price sort mode.