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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 251–300 of 753 papers

TitleStatusHype
Federated Unbiased Learning to Rank—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
Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets—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
A Knowledge Graph Based Solution for Entity Discovery and Linking in Open-Domain Questions—0
Inference-time Stochastic Ranking with Risk Control—0
Cascading Bandits Robust to Adversarial Corruptions—0
Factorizing LambdaMART for cold start recommendations—0
Factorization Machines Leveraging Lightweight Linked Open Data-enabled Features for Top-N Recommendations—0
Cascading Bandits: Learning to Rank in the Cascade Model—0
Factorization Machines for Data with Implicit Feedback—0
Extreme Learning to Rank via Low Rank Assumption—0
Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank—0
Extractive Headline Generation Based on Learning to Rank for Community Question Answering—0
Extraction of Domain-Specific Bilingual Lexicon from Comparable Corpora: Compositional Translation and Ranking—0
Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training—0
Answering questions by learning to rank -- Learning to rank by answering questions—0
AIBench: An Industry Standard Internet Service AI Benchmark Suite—0
Influence of Neighborhood on the Preference of an Item in eCommerce Search—0
Explore Entity Embedding Effectiveness in Entity Retrieval—0
Calibrating Explore-Exploit Trade-off for Fair Online Learning to Rank—0
Exploration of Unranked Items in Safe Online Learning to Re-Rank—0
Building Cross-Sectional Systematic Strategies By Learning to Rank—0
An IPW-based Unbiased Ranking Metric in Two-sided Markets—0
Explicit and Implicit Semantic Ranking Framework—0
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback—0
Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency—0
Expected Divergence Based Feature Selection for Learning to Rank—0
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