Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation

Researchers propose a metric-agnostic learning-to-rank approach using boosting and rank approximation to overcome limitations of single-metric optimization. The method addresses non-differentiability and limited ranking utility by enabling models to optimize across multiple ranking metrics simultaneously.

MentionsLearning-to-Rank · NDCG · Mean Average Precision

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