Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation
Learning-to-Rank (LTR) is a supervised machine learning approach that constructs models specifically designed to order a set of items or documents based on their relevance or importance to a given query or context. Despite significant success in real-world information retrieval systems, current LTR methods rely on one prefix ranking metric (e.g., such as Normalized Discounted Cumulative Gain (NDCG) or Mean Average Precision (MAP)) for optimizing the ranking objective function. Such metric-dependent setting limits LTR methods from two perspectives: (1) non-differentiable problem: directly optimizing ranking functions over a given ranking metric is inherently non-smooth, making the training...
| Authors | Camilo Gomez, Pengyang Wang, Yanjie Fu |
|---|---|
| Primary category | cs.IR |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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