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Learning-To-Rank

Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).

Papers

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Showing 276–300 of 753 papers

TitleStatusHype
FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank—0
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback—0
Learning to Rank Rationales for Explainable RecommendationCode0
Offline Evaluation of Ranked Lists using Parametric Estimation of Propensities—0
Pessimistic Off-Policy Optimization for Learning to Rank—0
Scalar is Not Enough: Vectorization-based Unbiased Learning to RankCode0
Glance to Count: Learning to Rank with Anchors for Weakly-supervised Crowd Counting—0
A Simple yet Effective Framework for Active Learning to Rank—0
Optimization of Decision Tree Evaluation Using SIMD InstructionsCode0
Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling—0
Compound virtual screening by learning-to-rank with gradient boosting decision tree and enrichment-based cumulative gain—0
Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency—0
Learning to Rank Visual Stories From Human Ranking DataCode0
Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in RankingCode0
MovieMat: Context-aware Movie Recommendation with Matrix Factorization by Matrix Fitting—0
Groupwise Query Performance Prediction with BERTCode0
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion—0
Is Non-IID Data a Threat in Federated Online Learning to Rank?Code0
Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank—0
Which Tricks Are Important for Learning to Rank?—0
Unbiased Top-k Learning to Rank with Causal Likelihood DecompositionCode0
Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to RankCode0
Minimax Regret for Cascading Bandits—0
Personalized Execution Time Optimization for the Scheduled Jobs—0
Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths—0
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