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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 101–150 of 753 papers

TitleStatusHype
Learning to Rank Patches for Unbiased Image Redundancy ReductionCode0
An Efficient Combinatorial Optimization Model Using Learning-to-Rank DistillationCode0
Learning to Rank Question Answer Pairs with Holographic Dual LSTM ArchitectureCode0
Maximizing Marginal Fairness for Dynamic Learning to RankCode0
Unbiased Learning to Rank with Unbiased Propensity EstimationCode0
Learning to rank for censored survival dataCode0
Learning to Rank from Relevance Judgments DistributionsCode0
A General Framework for Pairwise Unbiased Learning to RankCode0
Adversarial Mixture Of Experts with Category Hierarchy Soft ConstraintCode0
Model-based Unbiased Learning to RankCode0
Learning to Rank Context for Named Entity Recognition Using a Synthetic DatasetCode0
Learning to Rank Rationales for Explainable RecommendationCode0
A Probabilistic Position Bias Model for Short-Video Recommendation FeedsCode0
BEER 1.1: ILLC UvA submission to metrics and tuning taskCode0
Learning to Explain Entity Relationships in Knowledge GraphsCode0
Learning to Rank Ace Neural Architectures via Normalized Discounted Cumulative GainCode0
Learning-To-Rank Approach for Identifying Everyday Objects Using a Physical-World Search EngineCode0
Combinatorial Logistic BanditsCode0
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor SearchCode0
Optimizing Group-Fair Plackett-Luce Ranking Models for Relevance and Ex-Post FairnessCode0
PairRank: Online Pairwise Learning to Rank by Divide-and-ConquerCode0
Balancing Speed and Quality in Online Learning to Rank for Information RetrievalCode0
Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential RecommendationCode0
Learning a Deep Listwise Context Model for Ranking RefinementCode0
pNovo 3: precise de novo peptide sequencing using a learning-to-rank frameworkCode0
Policy-Aware Unbiased Learning to Rank for Top-k RankingsCode0
Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to RankCode0
LaSER: Language-Specific Event RecommendationCode0
Learning Cluster Representatives for Approximate Nearest Neighbor SearchCode0
Learning to Rank Aspects and Opinions for Comparative ExplanationsCode0
MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph FusionCode0
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?Code0
Autoregressive Reasoning over Chains of Facts with TransformersCode0
Is Non-IID Data a Threat in Federated Online Learning to Rank?Code0
Automatic Quality Estimation for Natural Language Generation: Ranting (Jointly Rating and Ranking)Code0
Intersection of Parallels as an Early Stopping CriterionCode0
Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility StudyCode0
Joint Optimization of Cascade Ranking ModelsCode0
ImitAL: Learning Active Learning Strategies from Synthetic DataCode0
How to Forget Clients in Federated Online Learning to Rank?Code0
Improving Pairwise Ranking for Multi-label Image ClassificationCode0
DCM Bandits: Learning to Rank with Multiple ClicksCode0
Hidden or Inferred: Fair Learning-To-Rank with Unknown DemographicsCode0
HAPI: A Model for Learning Robot Facial Expressions from Human PreferencesCode0
Groupwise Query Performance Prediction with BERTCode0
Hashing as Tie-Aware Learning to RankCode0
Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVMCode0
Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to RankCode0
ImitAL: Learned Active Learning Strategy on Synthetic DataCode0
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information SourcesCode0
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