Abstract
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.- Anthology ID:
- 2020.acl-main.614
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6861–6869
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.614
- DOI:
- 10.18653/v1/2020.acl-main.614
- Cite (ACL):
- Thanh Nguyen, Nikhil Rao, and Karthik Subbian. 2020. Learning Robust Models for e-Commerce Product Search. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6861–6869, Online. Association for Computational Linguistics.
- Cite (Informal):
- Learning Robust Models for e-Commerce Product Search (Nguyen et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.614.pdf