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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 251–260 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
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