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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 251–275 of 753 papers

TitleStatusHype
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
Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation With Minimal Computational ComplexityCode1
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
Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal RegressionCode1
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
Distilled Neural Networks for Efficient Learning to RankCode0
Learning to Rank from Relevance Judgments DistributionsCode0
Ultra-fine Entity Typing with Indirect Supervision from Natural Language InferenceCode1
A new perspective on classification: optimally allocating limited resources to uncertain tasks—0
Learning to Rank For Push Notifications Using Pairwise Expected Regret—0
Learning Neural Ranking Models Online from Implicit User Feedback—0
Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document—0
Reinforcement Online Learning to Rank with Unbiased Reward ShapingCode0
An Efficient Combinatorial Optimization Model Using Learning-to-Rank DistillationCode0
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