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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 151–200 of 753 papers

TitleStatusHype
FAQ-based Question Answering via Word Alignment—0
Inference-time Stochastic Ranking with Risk Control—0
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity—0
Fairness for Robust Learning to Rank—0
Bag-of-Words Forced Decoding for Cross-Lingual Information Retrieval—0
Baby Bear: Seeking a Just Right Rating Scale for Scalar Annotations—0
Dialog Generation Using Multi-Turn Reasoning Neural Networks—0
A Framework for Ranking Content Providers Using Prompt Engineering and Self-Attention Network—0
Factorizing LambdaMART for cold start recommendations—0
Fairness in Ranking: A Survey—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
Fast and Accurate Preordering for SMT using Neural Networks—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
Efficient and Accurate Top-K Recovery from Choice Data—0
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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