Ranking
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A repository of examples, utilities, and best practices for building and deploying production-ready recommendation systems.
A fast, distributed gradient boosting framework based on decision tree algorithms for ranking, classification, and other machine learning tasks.
A fast, distributed gradient boosting framework based on decision tree algorithms for ranking, classification, and other ML tasks.
A high-performance gradient boosting library with best-in-class handling of categorical features and support for CPU/GPU training.
A machine learning library designed for human interpretability, featuring debuggable models and a feature transform language.
A TensorFlow library for Learning-to-Rank (LTR) techniques, providing loss functions, metrics, and models for ranking tasks.
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