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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 51–75 of 753 papers

TitleStatusHype
Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)—0
Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)—0
LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs—0
Patch Ranking: Efficient CLIP by Learning to Rank Local PatchesCode0
Understanding the Effects of the Baidu-ULTR Logging Policy on Two-Tower Models—0
A Framework for Ranking Content Providers Using Prompt Engineering and Self-Attention Network—0
Proximal Ranking Policy Optimization for Practical Safety in Counterfactual Learning to Rank—0
Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity—0
Zeroshot Listwise Learning to Rank Algorithm for Recommendation—0
Efficient LLM Scheduling by Learning to RankCode2
Semi-Supervised Variational Adversarial Active Learning via Learning to Rank and Agreement-Based Pseudo Labeling—0
Baby Bear: Seeking a Just Right Rating Scale for Scalar Annotations—0
Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rank—0
Ranking Across Different Content Types: The Robust Beauty of Multinomial Blending—0
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading BanditsCode0
Pairwise Judgment Formulation for Semantic Embedding Model in Web Search—0
Set2Seq Transformer: Learning Permutation Aware Set Representations of Artistic Sequences—0
Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential RecommendationCode0
Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank—0
Hidden or Inferred: Fair Learning-To-Rank with Unknown DemographicsCode0
MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search—0
Multi-objective Learning to Rank by Model Distillation—0
Leveraging Topic Specificity and Social Relationships for Expert Finding in Community Question Answering PlatformsCode0
Deep Domain Specialisation for single-model multi-domain learning to rank—0
When Search Engine Services meet Large Language Models: Visions and Challenges—0
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