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 481–490 of 753 papers

TitleStatusHype
Non-convex Regularizations for Feature Selection in Ranking With Sparse SVM—0
Analysis of E-commerce Ranking Signals via Signal Temporal Logic—0
No-reference Screen Content Image Quality Assessment with Unsupervised Domain Adaptation—0
NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models—0
Offline Evaluation of Ranked Lists using Parametric Estimation of Propensities—0
Offline Learning for Combinatorial Multi-armed Bandits—0
An Alternative Cross Entropy Loss for Learning-to-Rank—0
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model—0
Two-Layer Generalization Analysis for Ranking Using Rademacher Average—0
On Application of Learning to Rank for E-Commerce Search—0
Show:102550
← PrevPage 49 of 76Next →

No leaderboard results yet.