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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
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity—0
End-to-end Learning for Fair Ranking Systems—0
Bag-of-Words Forced Decoding for Cross-Lingual Information Retrieval—0
Dialog Generation Using Multi-Turn Reasoning Neural Networks—0
A Framework for Ranking Content Providers Using Prompt Engineering and Self-Attention Network—0
Eliminating Search Intent Bias in Learning to Rank—0
Entailment-Preserving First-order Logic Representations in Natural Language Entailment—0
Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths—0
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss—0
Baby Bear: Seeking a Just Right Rating Scale for Scalar Annotations—0
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs—0
Position Bias Estimation for Unbiased Learning-to-Rank in eCommerce Search—0
Direct Learning to Rank and Rerank—0
Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events—0
Analysis of Regression Tree Fitting Algorithms in Learning to Rank—0
DocChat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents—0
Don't Just Pay Attention, PLANT It: Transfer L2R Models to Fine-tune Attention in Extreme Multi-Label Text Classification—0
Don't Mention the Shoe! A Learning to Rank Approach to Content Selection for Image Description Generation—0
BanditRank: Learning to Rank Using Contextual Bandits—0
Drug Selection via Joint Push and Learning to Rank—0
Detect2Rank : Combining Object Detectors Using Learning to Rank—0
BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale—0
ECNU at SemEval-2016 Task 7: An Enhanced Supervised Learning Method for Lexicon Sentiment Intensity Ranking—0
Effective and secure federated online learning to rank—0
Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity—0
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