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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 201–250 of 753 papers

TitleStatusHype
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback—0
Explicit and Implicit Semantic Ranking Framework—0
Building Cross-Sectional Systematic Strategies By Learning to Rank—0
Exploration of Unranked Items in Safe Online Learning to Re-Rank—0
Explore Entity Embedding Effectiveness in Entity Retrieval—0
Influence of Neighborhood on the Preference of an Item in eCommerce Search—0
Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity—0
Extractive Headline Generation Based on Learning to Rank for Community Question Answering—0
Extreme Learning to Rank via Low Rank Assumption—0
Factorization Machines for Data with Implicit Feedback—0
Factorization Machines Leveraging Lightweight Linked Open Data-enabled Features for Top-N Recommendations—0
Factorizing LambdaMART for cold start recommendations—0
Cascading Bandits Robust to Adversarial Corruptions—0
Inference-time Stochastic Ranking with Risk Control—0
Fairness for Robust Learning to Rank—0
Fairness in Ranking: A Survey—0
Fairness Through Regularization for Learning to Rank—0
FAIR-QR: Enhancing Fairness-aware Information Retrieval through Query Refinement—0
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering forWAT 2015—0
A Passage-Based Approach to Learning to Rank Documents—0
FAQ-based Question Answering via Word Alignment—0
Fast and Accurate Preordering for SMT using Neural Networks—0
Deep Ranking for Person Re-identification via Joint Representation Learning—0
FAST: Financial News and Tweet Based Time Aware Network for Stock Trading—0
Feature Engineering in Learning-to-Rank for Community Question Answering Task—0
Feature-Enhanced Network with Hybrid Debiasing Strategies for Unbiased Learning to Rank—0
Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets—0
Federated Unbiased Learning to Rank—0
Fine-grained Emotional Control of Text-To-Speech: Learning To Rank Inter- And Intra-Class Emotion Intensities—0
Deep Ranking Ensembles for Hyperparameter Optimization—0
Analysis of E-commerce Ranking Signals via Signal Temporal Logic—0
FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank—0
Forest Reranking through Subtree Ranking—0
From Protocol to Screening: A Hybrid Learning Approach for Technology-Assisted Systematic Literature Reviews—0
ICLERB: In-Context Learning Embedding and Reranker Benchmark—0
Full Stage Learning to Rank: A Unified Framework for Multi-Stage Systems—0
GABAR: Graph Attention-Based Action Ranking for Relational Policy Learning—0
Generalization error bounds for learning to rank: Does the length of document lists matter?—0
Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)—0
Glance to Count: Learning to Rank with Anchors for Weakly-supervised Crowd Counting—0
Deep Pairwise Learning To Rank For Search Autocomplete—0
Global Ranking Using Continuous Conditional Random Fields—0
GotFunding: A grant recommendation system based on scientific articles—0
GPKEX: Genetically Programmed Keyphrase Extraction from Croatian Texts—0
Deep Neural Network for Learning to Rank Query-Text Pairs—0
Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph—0
GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment—0
The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection—0
GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking—0
An Alternative Cross Entropy Loss for Learning-to-Rank—0
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