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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
Content-Based Features to Rank Influential Hidden Services of the Tor Darknet—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
Content Selection for Real-time Sports News Construction from Commentary Texts—0
An Alternative Cross Entropy Loss for Learning-to-Rank—0
Deep Multi-view Learning to Rank—0
Automated Essay Scoring by Maximizing Human-Machine Agreement—0
A Flexible Recommendation System for Cable TV—0
Joint Upper & Lower Bound Normalization for IR Evaluation—0
Deep Domain Specialisation for single-model multi-domain learning to rank—0
Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity—0
Automated Disease Normalization with Low Rank Approximations—0
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data—0
De-Biased Modelling of Search Click Behavior with Reinforcement Learning—0
autoBagging: Learning to Rank Bagging Workflows with Metalearning—0
A Multi-Perspective Learning to Rank Approach to Support Children's Information Seeking in the Classroom—0
AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment—0
A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization—0
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer—0
CRST: a Claim Retrieval System in Twitter—0
Attention-based neural re-ranking approach for next city in trip recommendations—0
Adversarial Attacks on Online Learning to Rank with Stochastic Click Models—0
Invited Talk: Learning from Rational Behavior—0
Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions—0
Cross-lingual Subjectivity Detection for Resource Lean Languages—0
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