Training Efficient Tree-Based Models for Document Ranking

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Off-policy evaluation for slate recommendation (2016, methodology): “The state-of-the-art listwise L2R method on this dataset is a highly tuned variant of LambdaMART [1] (with an ensemble of 1000 trees, each with up to 70 leaves).”
Quality versus efficiency in document scoring with learning-to-rank models (2016, unknown): “When processing N φ in ascending order by γ , as soon as a test evaluates to true, i.e., x [ φ ] ≤ γ , the remaining occurrences surely evaluate to true as well, and their evaluation is thus safely skipped.”