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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
Efficient and Consistent Adversarial Bipartite Matching—0
Efficient and Effective Tree-based and Neural Learning to Rank—0
Efficient and Responsible Adaptation of Large Language Models for Robust Top-k Recommendations—0
Feature Engineering in Learning-to-Rank for Community Question Answering Task—0
Efficient Exploration of Gradient Space for Online Learning to Rank—0
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss—0
Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation—0
Efficient support ticket resolution using Knowledge Graphs—0
EILEEN: A recommendation system for scientific publications and grants—0
Eliminating Search Intent Bias in Learning to Rank—0
Embedding Meta-Textual Information for Improved Learning to Rank—0
End-to-end Learning for Fair Ranking Systems—0
Boosting API Recommendation with Implicit Feedback—0
Detect2Rank : Combining Object Detectors Using Learning to Rank—0
Enhancing LambdaMART Using Oblivious Trees—0
Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity—0
Deep Ranking for Person Re-identification via Joint Representation Learning—0
Entailment-Preserving First-order Logic Representations in Natural Language Entailment—0
Bounded-Abstention Pairwise Learning to Rank—0
Estimating Position Bias without Intrusive Interventions—0
A new perspective on classification: optimally allocating limited resources to uncertain tasks—0
Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths—0
Bridging the Gap: Incorporating a Semantic Similarity Measure for Effectively Mapping PubMed Queries to Documents—0
Expected Divergence Based Feature Selection for Learning to Rank—0
Deep Ranking Ensembles for Hyperparameter Optimization—0
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