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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 551–600 of 753 papers

TitleStatusHype
iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making—0
Distractor Generation for Multiple Choice Questions Using Learning to RankCode0
Cross-Lingual Learning-to-Rank with Shared Representations—0
Dialog Generation Using Multi-Turn Reasoning Neural Networks—0
Efficient Exploration of Gradient Space for Online Learning to Rank—0
Ranking for Relevance and Display Preferences in Complex Presentation LayoutsCode0
Weakly-supervised Contextualization of Knowledge Graph Facts—0
Semantic Relatedness of Wikipedia Concepts -- Benchmark Data and a Working Solution—0
Semi-Automatic Construction of Word-Formation Networks (for Polish and Spanish)—0
A General Framework for Counterfactual Learning-to-Rank—0
Learning a Deep Listwise Context Model for Ranking RefinementCode0
Unbiased Learning to Rank with Unbiased Propensity EstimationCode0
Application of the Ranking Relative Principal Component Attributes Network Model (REL-PCANet) for the Inclusive Development Index Estimation—0
Local Descriptors Optimized for Average Precision—0
Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal PatternsCode0
Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets—0
Leveraging Unlabeled Data for Crowd Counting by Learning to RankCode0
Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and ApplicationCode0
Deep Neural Network for Learning to Rank Query-Text Pairs—0
Direct Learning to Rank and Rerank—0
Web-Scale Responsive Visual Search at Bing—0
Convolutional Neural Networks for Soft Matching N-Grams in Ad-hoc Search—0
Deep Multi-view Learning to Rank—0
Assertion-based QA with Question-Aware Open Information Extraction—0
Drug Selection via Joint Push and Learning to Rank—0
Learning to Select: Problem, Solution, and Applications—0
PRUNE: Preserving Proximity and Global Ranking for Network EmbeddingCode0
Learning to Rank based on Analogical Reasoning—0
Balancing Speed and Quality in Online Learning to Rank for Information RetrievalCode0
Neural Ranking Models with Multiple Document Fields—0
On the ERM Principle with Networked Data—0
WMRB: Learning to Rank in a Scalable Batch Training Approach—0
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information SourcesCode0
Learning Visual Features from Snapshots for Web Search—0
Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic ClusteringCode1
Content Selection for Real-time Sports News Construction from Commentary Texts—0
Ranking Kernels for Structures and Embeddings: A Hybrid Preference and Classification Model—0
Learning to Rank Semantic Coherence for Topic Segmentation—0
Improved Answer Selection with Pre-Trained Word Embeddings—0
LearningToQuestion at SemEval 2017 Task 3: Ranking Similar Questions by Learning to Rank Using Rich Features—0
Beihang-MSRA at SemEval-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering—0
Factorization Machines Leveraging Lightweight Linked Open Data-enabled Features for Top-N Recommendations—0
A Deep Investigation of Deep IR Models—0
Modeling Label Ambiguity for Neural List-Wise Learning to RankCode0
Learning to Rank Question Answer Pairs with Holographic Dual LSTM ArchitectureCode0
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer—0
Learning to Rank Retargeted Images—0
A Network Framework for Noisy Label Aggregation in Social Media—0
Alternative Objective Functions for Training MT Evaluation Metrics—0
autoBagging: Learning to Rank Bagging Workflows with Metalearning—0
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