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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 451–500 of 753 papers

TitleStatusHype
Team SVMrank: Leveraging Feature-rich Support Vector Machines for Ranking Explanations to Elementary Science Questions—0
The DipInfoUniTo Realizer at SRST'19: Learning to Rank and Deep Morphology Prediction for Multilingual Surface Realization—0
BanditRank: Learning to Rank Using Contextual Bandits—0
Self-Attentive Document Interaction Networks for Permutation Equivariant Ranking—0
Universal Text Representation from BERT: An Empirical Study—0
Personalized Context-Aware Multi-Modal Transportation Recommendation—0
Automatic Quality Estimation for Natural Language Generation: Ranting (Jointly Rating and Ranking)Code0
Content-Based Features to Rank Influential Hidden Services of the Tor Darknet—0
Learning to Rank Proposals for Object Detection—0
Learning Effective Exploration Strategies For Contextual Bandits—0
ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking—0
MarlRank: Multi-agent Reinforced Learning to Rank—0
Plackett-Luce model for learning-to-rank task—0
Analysis of Regression Tree Fitting Algorithms in Learning to Rank—0
Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical PerformanceCode0
Uncertain Natural Language Inference—0
Answering questions by learning to rank -- Learning to rank by answering questions—0
Explore Entity Embedding Effectiveness in Entity Retrieval—0
A Study of BERT for Non-Factoid Question-Answering under Passage Length Constraints—0
A Machine Learning Approach for Smartphone-based Sensing of Roads and Driving Style—0
AIBench: An Industry Standard Internet Service AI Benchmark Suite—0
Influence of Neighborhood on the Preference of an Item in eCommerce Search—0
FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social FeedsCode0
Reward Learning for Efficient Reinforcement Learning in Extractive Document SummarisationCode0
Mend The Learning Approach, Not the Data: Insights for Ranking E-Commerce ProductsCode0
Differentially Private Link Prediction With Protected Connections—0
Learning More From Less: Towards Strengthening Weak Supervision for Ad-Hoc Retrieval—0
Unbiased Learning to Rank: Counterfactual and Online Approaches—0
To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User InteractionsCode0
pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework—0
Learning to Blindly Assess Image Quality in the Laboratory and WildCode1
Learning to Rank Broad and Narrow Queries in E-Commerce—0
Practical User Feedback-driven Internal Search Using Online Learning to Rank—0
Microsoft AI Challenge India 2018: Learning to Rank Passages for Web Question Answering with Deep Attention Networks—0
Towards Amortized Ranking-Critical Training for Collaborative FilteringCode1
Variance Reduction in Gradient Exploration for Online Learning to Rank—0
Learning to Rank for Plausible Plausibility—0
A Passage-Based Approach to Learning to Rank Documents—0
Cross-lingual Subjectivity Detection for Resource Lean Languages—0
A Study of Latent Structured Prediction Approaches to Passage Reranking—0
Deep Metric Learning to RankCode0
Uncoupled Regression from Pairwise Comparison DataCode0
Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model—0
Spectrum-enhanced Pairwise Learning to Rank—0
Block-distributed Gradient Boosted Trees—0
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs—0
ViTOR: Learning to Rank Webpages Based on Visual Features—0
On Application of Learning to Rank for E-Commerce Search—0
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to RankCode0
A Domain Generalization Perspective on Listwise Context Modeling—0
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