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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

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Showing 126–150 of 753 papers

TitleStatusHype
Replicating Relevance-Ranked Synonym Discovery in a New Language and Domain—0
SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data—0
Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to RankCode0
NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models—0
Feature Engineering in Learning-to-Rank for Community Question Answering Task—0
A Multi-Perspective Learning to Rank Approach to Support Children's Information Seeking in the Classroom—0
TRIVEA: Transparent Ranking Interpretation using Visual Explanation of Black-Box Algorithmic Rankers—0
Optimizing Group-Fair Plackett-Luce Ranking Models for Relevance and Ex-Post FairnessCode0
The Impact of Group Membership Bias on the Quality and Fairness of Exposure in Ranking—0
Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank—0
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity—0
Sustainable transparency in Recommender Systems: Bayesian Ranking of Images for ExplainabilityCode0
A Probabilistic Position Bias Model for Short-Video Recommendation FeedsCode0
MIST-CF: Chemical formula inference from tandem mass spectraCode1
An IPW-based Unbiased Ranking Metric in Two-sided Markets—0
A Machine-Learned Ranking Algorithm for Dynamic and Personalised Car Pooling Services—0
An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems—0
Understanding User Behavior in Carousel Recommendation Systems for Click Modeling and Learning to Rank—0
THUIR2 at NTCIR-16 Session Search (SS) Task—0
Learning to Rank in Generative RetrievalCode1
Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal ClassificationCode1
A Reference-less Quality Metric for Automatic Speech Recognition via Contrastive-Learning of a Multi-Language Model with Self-SupervisionCode1
NoRefER: a Referenceless Quality Metric for Automatic Speech Recognition via Semi-Supervised Language Model Fine-Tuning with Contrastive LearningCode1
Learning to Rank when Grades Matter—0
Unified Off-Policy Learning to Rank: a Reinforcement Learning PerspectiveCode0
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