Industry Insights from Comparing Deep Learning and GBDT Models for E-Commerce Learning-to-Rank
Abstract
In e-commerce recommender and search systems, tree-based models, such as LambdaMART, have set a strong baseline for Learning-to-Rank (LTR) tasks. Despite their effectiveness and widespread adoption in industry, the debate continues whether deep neural networks (DNNs) can outperform traditional tree-based models in this domain. To contribute to this discussion, we systematically benchmark DNNs against our production-grade LambdaMART model. We evaluate multiple DNN architectures and loss functions on a proprietary dataset from OTTO and validate our findings through an 8-week online A/B test. The results show that a simple DNN architecture outperforms a strong tree-based baseline in terms of total clicks and revenue, while achieving parity in total units sold.
Cite
@article{arxiv.2507.20753,
title = {Industry Insights from Comparing Deep Learning and GBDT Models for E-Commerce Learning-to-Rank},
author = {Yunus Lutz and Timo Wilm and Philipp Duwe},
journal= {arXiv preprint arXiv:2507.20753},
year = {2025}
}
Comments
This work was accepted for publication in the 19th ACM Conference on Recommender Systems (RecSys 2025). The final published version will be available at the ACM Digital Library