Industry Insights from Comparing Deep Learning and GBDT Models for E-Commerce Learning-to-Rank
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| Format: | Preprint |
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2025
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| _version_ | 1866912505354256384 |
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| author | Lutz, Yunus Wilm, Timo Duwe, Philipp |
| author_facet | Lutz, Yunus Wilm, Timo Duwe, Philipp |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_20753 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Industry Insights from Comparing Deep Learning and GBDT Models for E-Commerce Learning-to-Rank Lutz, Yunus Wilm, Timo Duwe, Philipp Information Retrieval Artificial Intelligence Machine Learning 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. |
| title | Industry Insights from Comparing Deep Learning and GBDT Models for E-Commerce Learning-to-Rank |
| topic | Information Retrieval Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.20753 |