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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 151–175 of 753 papers

TitleStatusHype
Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility StudyCode0
Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search DatasetCode0
Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval—0
Learning to Rank Patches for Unbiased Image Redundancy ReductionCode0
RankingSHAP -- Listwise Feature Attribution Explanations for Ranking ModelsCode0
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMsCode0
Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages—0
List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented GenerationCode0
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning—0
ShaRP: A Novel Feature Importance Framework for RankingCode0
How to Forget Clients in Federated Online Learning to Rank?Code0
InfoRank: Unbiased Learning-to-Rank via Conditional Mutual Information Minimization—0
Towards Off-Policy Reinforcement Learning for Ranking Policies with Human Feedback—0
Learning-to-Rank with Nested Feedback—0
Learning-To-Rank Approach for Identifying Everyday Objects Using a Physical-World Search EngineCode0
FSscore: A Machine Learning-based Synthetic Feasibility Score Leveraging Human Expertise—0
A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization—0
SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive EnvironmentsCode0
Bandit Learning to Rank with Position-Based Click Models: Personalized and Equal Treatments—0
SortNet: Learning To Rank By a Neural-Based Sorting Algorithm—0
Unbiased Offline Evaluation for Learning to Rank with Business Rules—0
RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data ManipulationCode0
Learning to Rank for Active Learning via Multi-Task Bilevel Optimization—0
Bi-Encoders based Species Normalization -- Pairwise Sentence Learning to Rank—0
An Exploratory Study on Simulated Annealing for Feature Selection in Learning-to-Rank—0
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