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 501–550 of 753 papers

TitleStatusHype
On the Calibration and Uncertainty of Neural Learning to Rank Models for Conversational Search—0
On the Consistency of AUC Pairwise Optimization—0
On the ERM Principle with Networked Data—0
ULTRA: An Unbiased Learning To Rank Algorithm Toolbox—0
On The Structure of Parametric Tournaments with Application to Ranking from Pairwise Comparisons—0
Ontology-Based Retrieval \& Neural Approaches for BioASQ Ideal Answer Generation—0
OPI at SemEval 2023 Task 1: Image-Text Embeddings and Multimodal Information Retrieval for Visual Word Sense Disambiguation—0
Activity Auto-Completion: Predicting Human Activities From Partial Videos—0
Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation—0
Optimize What You Evaluate With: A Simple Yet Effective Framework For Direct Optimization Of IR Metrics—0
Weakly-supervised Contextualization of Knowledge Graph Facts—0
Optimizing Preference Alignment with Differentiable NDCG Ranking—0
Optimizing Ranking Models in an Online Setting—0
Optimizing Ranking Systems Online as Bandits—0
Unbiased Learning to Rank: Counterfactual and Online Approaches—0
Overview of the CLEF-2019 CheckThat!: Automatic Identification and Verification of Claims—0
Neural IR Meets Graph Embedding: A Ranking Model for Product Search—0
Pairwise Judgment Formulation for Semantic Embedding Model in Web Search—0
A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization—0
Unbiased Learning to Rank: Online or Offline?—0
Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions—0
Par4Sim -- Adaptive Paraphrasing for Text Simplification—0
Pareto Pairwise Ranking for Fairness Enhancement of Recommender Systems—0
Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction—0
Unbiased Learning-to-Rank with Biased Feedback—0
Unbiased Learning to Rank with Biased Continuous Feedback—0
Perceptron-like Algorithms and Generalization Bounds for Learning to Rank—0
Perceptron like Algorithms for Online Learning to Rank—0
Personalized Context-Aware Multi-Modal Transportation Recommendation—0
Personalized Context-Aware Point of Interest Recommendation—0
Personalized Execution Time Optimization for the Scheduled Jobs—0
Cognitive Biomarker Prioritization in Alzheimer's Disease using Brain Morphometric Data—0
Pessimistic Off-Policy Optimization for Learning to Rank—0
A Machine Learning Approach for Smartphone-based Sensing of Roads and Driving Style—0
Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback—0
Plackett-Luce model for learning-to-rank task—0
Weak Supervision for Improved Precision in Search Systems—0
Unbiased Offline Evaluation for Learning to Rank with Business Rules—0
Web-Scale Responsive Visual Search at Bing—0
Posthoc Interpretability of Learning to Rank Models using Secondary Training Data—0
Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank—0
A Simple yet Effective Framework for Active Learning to Rank—0
Practical User Feedback-driven Internal Search Using Online Learning to Rank—0
A Machine-Learned Ranking Algorithm for Dynamic and Personalised Car Pooling Services—0
Predtron: A Family of Online Algorithms for General Prediction Problems—0
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity—0
Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)—0
Differentially Private Link Prediction With Protected Connections—0
"What Are You Trying to Do?" Semantic Typing of Event Processes—0
Proximal Ranking Policy Optimization for Practical Safety in Counterfactual Learning to Rank—0
Show:102550
← PrevPage 11 of 16Next →

No leaderboard results yet.