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arXiv · 2507.20753

Industry Insights from Comparing Deep Learning and GBDT Models for E-Commerce Learning-to-Rank

Yunus Lutz·Timo Wilm·Philipp Duwe
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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.

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Yunus Lutz, Timo Wilm, Philipp Duwe. 2025-07-28. Industry Insights from Comparing Deep Learning and GBDT Models for E-Commerce Learning-to-Rank. https://doi.org/10.1145/3705328.3748130

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