AI RESEARCH
Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation
arXiv CS.LG
•
ArXi:2604.15101v1 Announce Type: cross 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.