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 71–80 of 753 papers

TitleStatusHype
Learning Cluster Representatives for Approximate Nearest Neighbor SearchCode0
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor SearchCode0
A Recurrent Model for Collective Entity Linking with Adaptive FeaturesCode0
Learning to Rank Ace Neural Architectures via Normalized Discounted Cumulative GainCode0
A Probabilistic Position Bias Model for Short-Video Recommendation FeedsCode0
Joint Optimization of Cascade Ranking ModelsCode0
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information SourcesCode0
Intersection of Parallels as an Early Stopping CriterionCode0
Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVMCode0
Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility StudyCode0
Show:102550
← PrevPage 8 of 76Next →

No leaderboard results yet.