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

2 citations

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Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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2 papers

cs.IR2025★ 2 cited

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

Yunus Lutz, Timo Wilm, Philipp Duwe

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 wi…

cs.IR2025

Identifying Offline Metrics that Predict Online Impact: A Pragmatic Strategy for Real-World Recommender Systems

Timo Wilm, Philipp Normann

A critical challenge in recommender systems is to establish reliable relationships between offline and online metrics that predict real-world performance. Motivated by recent advan…