arXiv Open Access 2025

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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Abstrak

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.

Topik & Kata Kunci

cs.IR cs.AI cs.LG

Penulis (3)

Yunus Lutz

Timo Wilm

Philipp Duwe

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APA MLA BibTeX

Lutz, Y., Wilm, T., Duwe, P. (2025). Industry Insights from Comparing Deep Learning and GBDT Models for E-Commerce Learning-to-Rank. https://arxiv.org/abs/2507.20753

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Informasi Jurnal

Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓