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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 176–200 of 753 papers

TitleStatusHype
Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems—0
Learning to Rank Context for Named Entity Recognition Using a Synthetic DatasetCode0
Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition—0
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
Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank—0
The Impact of Group Membership Bias on the Quality and Fairness of Exposure in Ranking—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
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 when Grades Matter—0
Unified Off-Policy Learning to Rank: a Reinforcement Learning PerspectiveCode0
Inference-time Stochastic Ranking with Risk Control—0
Skellam Rank: Fair Learning to Rank Algorithm Based on Poisson Process and Skellam Distribution for Recommender Systems—0
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