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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

Showing 601650 of 753 papers

TitleStatusHype
Personalized Context-Aware Multi-Modal Transportation Recommendation0
Personalized Context-Aware Point of Interest Recommendation0
Personalized Execution Time Optimization for the Scheduled Jobs0
Cognitive Biomarker Prioritization in Alzheimer's Disease using Brain Morphometric Data0
Pessimistic Off-Policy Optimization for Learning to Rank0
Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback0
Plackett-Luce model for learning-to-rank task0
pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework0
Is Non-IID Data a Threat in Federated Online Learning to Rank?Code0
Joint Optimization of Cascade Ranking ModelsCode0
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information SourcesCode0
Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal PatternsCode0
Maximizing Marginal Fairness for Dynamic Learning to RankCode0
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?Code0
Balancing Speed and Quality in Online Learning to Rank for Information RetrievalCode0
Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility StudyCode0
Few-Shot Text Ranking with Meta Adapted Synthetic Weak SupervisionCode0
Support vector comparison machinesCode0
Reward Learning for Efficient Reinforcement Learning in Extractive Document SummarisationCode0
LaSER: Language-Specific Event RecommendationCode0
Policy-Gradient Training of Fair and Unbiased Ranking FunctionsCode0
Distilled Neural Networks for Efficient Learning to RankCode0
Metric Learning for Session-based RecommendationsCode0
Learning a Deep Listwise Context Model for Ranking RefinementCode0
Learning Cluster Representatives for Approximate Nearest Neighbor SearchCode0
Sustainable transparency in Recommender Systems: Bayesian Ranking of Images for ExplainabilityCode0
Robust Generalization and Safe Query-Specialization in Counterfactual Learning to RankCode0
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor SearchCode0
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to RankCode0
When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to RankCode0
Intersection of Parallels as an Early Stopping CriterionCode0
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMsCode0
Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVMCode0
Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in RankingCode0
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading BanditsCode0
Mixture-Based Correction for Position and Trust Bias in Counterfactual Learning to RankCode0
Adversarial Mixture Of Experts with Category Hierarchy Soft ConstraintCode0
Model-based Unbiased Learning to RankCode0
TF-Ranking: Scalable TensorFlow Library for Learning-to-RankCode0
Modeling Label Ambiguity for Neural List-Wise Learning to RankCode0
PRUNE: Preserving Proximity and Global Ranking for Network EmbeddingCode0
Exact Passive-Aggressive Algorithms for Learning to Rank Using Interval LabelsCode0
Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk MinimizationCode0
Differentiable Unbiased Online Learning to RankCode0
Improving Pairwise Ranking for Multi-label Image ClassificationCode0
Safe Exploration for Optimizing Contextual BanditsCode0
More Accurate Question Answering on FreebaseCode0
Learning to Explain Entity Relationships in Knowledge GraphsCode0
ImitAL: Learning Active Learning Strategies from Synthetic DataCode0
Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative StudyCode0
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