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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 251–300 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
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion—0
Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation With Minimal Computational ComplexityCode1
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
Rank4Class: A Ranking Formulation for Multiclass Classification—0
Fairness for Robust Learning to Rank—0
Universalizing Weak Supervision—0
Decision-Focused Learning: Through the Lens of Learning to RankCode1
Pairwise Learning for Neural Link PredictionCode1
On The Structure of Parametric Tournaments with Application to Ranking from Pairwise Comparisons—0
Unbiased Pairwise Learning to Rank in Recommender SystemsCode0
End-to-end Learning for Fair Ranking Systems—0
Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph—0
Learning to Rank Visual Stories From Human Ranking Data—0
Learning to Rank in the Age of Muppets: Effectiveness–Efficiency Tradeoffs in Multi-Stage Ranking—0
Calibrating Explore-Exploit Trade-off for Fair Online Learning to Rank—0
A scale invariant ranking function for learning-to-rank: a real-world use case—0
EILEEN: A recommendation system for scientific publications and grants—0
Ranking Facts for Explaining Answers to Elementary Science Questions—0
Language Modelling via Learning to Rank—0
Optimizing Ranking Systems Online as Bandits—0
RoomStructNet: Learning to Rank Non-Cuboidal Room Layouts From Single View—0
Improving Neural Ranking via Lossless Knowledge Distillation—0
Learning-to-Count by Learning-to-Rank: Weakly Supervised Object Counting & Localization Using Only Pairwise Image Rankings—0
Rank4Class: Examining Multiclass Classification through the Lens of Learning to Rank—0
Overview of the CLEF-2019 CheckThat!: Automatic Identification and Verification of Claims—0
Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics—0
Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a DocumentCode0
Online Learning of Optimally Diverse Rankings—0
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