Tag
learning-to-rank
- Apr 27, 2026 Score Calibration for Blended Ranking Why a ranker optimized for ordering produces scores with no absolute meaning, how to fit a monotone calibration, and what breaks when the query mix shifts. information retrieval
- Sep 18, 2025 Query-Document Feature Extraction for Learned Ranking How match statistics, proximity windows, and coverage ratios are computed from position lists, and what each feature measures that a bag-of-words score cannot. information retrieval
- Feb 16, 2024 Personalization Signals and Click-Based Reranking How a search engine folds a user history and click behaviour into a ranking that was previously query-only, and what the feature vectors and the training targets actually look like. information retrieval
- Nov 15, 2023 LambdaMART and Metric-Driven Gradient Boosting How lambda gradients weight document pairs by their effect on NDCG, why the resulting tree ensemble is trained on pseudo-residuals, and where the heuristic gradient fails. information retrieval
- Apr 25, 2022 Pointwise, Pairwise, and Listwise Training Objectives What each learning-to-rank formulation optimizes, why pairwise ranking losses match the evaluation better than regression, and what listwise gradients buy. information retrieval