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 571–580 of 753 papers

TitleStatusHype
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
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model—0
On Application of Learning to Rank for E-Commerce Search—0
On Learning to Rank Long Sequences with Contextual Bandits—0
Online Diverse Learning to Rank from Partial-Click Feedback—0
Online Learning of Optimally Diverse Rankings—0
Online Learning to Rank in Stochastic Click Models—0
Online Learning to Rank with Features—0
Show:102550
← PrevPage 58 of 76Next →

No leaderboard results yet.