# Learning Robust Models for e-Commerce Product Search

arXiv: https://arxiv.org/abs/2005.03624 · HTML: https://mlsanity.com/paper/2005.03624/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 4 of 4)

- [Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations](https://mlsanity.com/paper/2104.02667/) (2021)
- [EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction](https://mlsanity.com/paper/2406.06839/) (2024)
- [Exploring Large Language Models for Product Attribute Value Identification](https://mlsanity.com/paper/2409.12695/) (2024)
- [Towards Better Query Classification with Multi-Expert Knowledge Condensation in JD Ads Search](https://mlsanity.com/paper/2308.01098/) (2023)

## Abstract (arXiv)

Showing items that do not match search query intent degrades customer experience in e-commerce. These mismatches result from counterfactual biases of the ranking algorithms toward noisy behavioral signals such as clicks and purchases in the search logs. Mitigating the problem requires a large labeled dataset, which is expensive and time-consuming to obtain. In this paper, we develop a deep, end-to-end model that learns to effectively classify mismatches and to generate hard mismatched examples to improve the classifier. We train the model end-to-end by introducing a latent variable into the cross-entropy loss that alternates between using the real and generated samples. This not only makes the classifier more robust but also boosts the overall ranking performance. Our model achieves a relative gain compared to baselines by over 26% in F-score, and over 17% in Area Under PR curve. On live search traffic, our model gains significant improvement in multiple countries.