SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 191–200 of 753 papers

TitleStatusHype
Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity—0
Deep Ranking for Person Re-identification via Joint Representation Learning—0
Entailment-Preserving First-order Logic Representations in Natural Language Entailment—0
Bounded-Abstention Pairwise Learning to Rank—0
Estimating Position Bias without Intrusive Interventions—0
A new perspective on classification: optimally allocating limited resources to uncertain tasks—0
Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths—0
Bridging the Gap: Incorporating a Semantic Similarity Measure for Effectively Mapping PubMed Queries to Documents—0
Expected Divergence Based Feature Selection for Learning to Rank—0
Deep Ranking Ensembles for Hyperparameter Optimization—0
Show:102550
← PrevPage 20 of 76Next →

No leaderboard results yet.