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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 251–275 of 753 papers

TitleStatusHype
Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets—0
Handling Class Imbalance in Link Prediction using Learning to Rank Techniques—0
Handling Position Bias for Unbiased Learning to Rank in Hotels Search—0
CICBUAPnlp: Graph-Based Approach for Answer Selection in Community Question Answering Task—0
Feature-Enhanced Network with Hybrid Debiasing Strategies for Unbiased Learning to Rank—0
Feature Engineering in Learning-to-Rank for Community Question Answering Task—0
Choice by Elimination via Deep Neural Networks—0
FAST: Financial News and Tweet Based Time Aware Network for Stock Trading—0
Fast and Memory-Efficient Neural Code Completion—0
Chiplet Placement Order Exploration Based on Learning to Rank with Graph Representation—0
Application of the Ranking Relative Principal Component Attributes Network Model (REL-PCANet) for the Inclusive Development Index Estimation—0
A Learning-to-Rank Approach for Image Color Enhancement—0
Addressing Purchase-Impression Gap through a Sequential Re-ranker—0
Fast and Accurate Preordering for SMT using Neural Networks—0
FAQ-based Question Answering via Word Alignment—0
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering for WAT 2016—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
FAIR-QR: Enhancing Fairness-aware Information Retrieval through Query Refinement—0
Fairness Through Regularization for Learning to Rank—0
Challenges in clinical natural language processing for automated disorder normalization—0
Fairness in Ranking: A Survey—0
Fairness for Robust Learning to Rank—0
Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model—0
Answering questions by learning to rank - Learning to rank by answering questions—0
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