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 21–30 of 753 papers

TitleStatusHype
A Reference-less Quality Metric for Automatic Speech Recognition via Contrastive-Learning of a Multi-Language Model with Self-SupervisionCode1
RankFormer: Listwise Learning-to-Rank Using Listwide LabelsCode1
RankCSE: Unsupervised Sentence Representations Learning via Learning to RankCode1
THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained Language Models for Legal Case RetrievalCode1
THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case EntailmentCode1
THUIR at WSDM Cup 2023 Task 1: Unbiased Learning to RankCode1
Lero: A Learning-to-Rank Query OptimizerCode1
RankDNN: Learning to Rank for Few-shot LearningCode1
RankCSE: Unsupervised Representation Learning via Learning to RankCode1
Supervised Metric Learning to Rank for Retrieval via Contextual Similarity OptimizationCode1
Show:102550
← PrevPage 3 of 76Next →

No leaderboard results yet.