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 211–220 of 753 papers

TitleStatusHype
Factorization Machines Leveraging Lightweight Linked Open Data-enabled Features for Top-N Recommendations—0
Factorizing LambdaMART for cold start recommendations—0
Cascading Bandits Robust to Adversarial Corruptions—0
Inference-time Stochastic Ranking with Risk Control—0
Fairness for Robust Learning to Rank—0
Fairness in Ranking: A Survey—0
Fairness Through Regularization for Learning to Rank—0
FAIR-QR: Enhancing Fairness-aware Information Retrieval through Query Refinement—0
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering forWAT 2015—0
Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets—0
Show:102550
← PrevPage 22 of 76Next →

No leaderboard results yet.