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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
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
Learning Rank Functionals: An Empirical Study—0
Learning Term Weights for Ad-hoc Retrieval—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
Learning More From Less: Towards Strengthening Weak Supervision for Ad-Hoc Retrieval—0
Learning Modulo Theories for preference elicitation in hybrid domains—0
Learning Neural Ranking Models Online from Implicit User Feedback—0
Adversarial Attacks on Online Learning to Rank with Stochastic Click Models—0
Attention-based neural re-ranking approach for next city in trip recommendations—0
Learning Minimum Volume Sets and Anomaly Detectors from KNN Graphs—0
Learning Optimal Card Ranking from Query Reformulation—0
Learning Paraphrasing for Multiword Expressions—0
Learning the Peculiar Value of Actions—0
A Survey on E-Commerce Learning to Rank—0
Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank—0
Information Ranking Using Optimum-Path Forest—0
Cross-Lingual Learning-to-Rank with Shared Representations—0
Learning from User Interactions with Rankings: A Unification of the Field—0
InfoRank: Unbiased Learning-to-Rank via Conditional Mutual Information Minimization—0
Influence Diagram Bandits—0
Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network—0
Interpretable Learning-to-Rank with Generalized Additive Models—0
Cross-lingual Subjectivity Detection for Resource Lean Languages—0
Intervention Harvesting for Context-Dependent Examination-Bias Estimation—0
CRST: a Claim Retrieval System in Twitter—0
Invited Talk: Learning from Rational Behavior—0
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer—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
Inducing Clause-Combining Rules: A Case Study with the SPaRKy Restaurant Corpus—0
Joint Upper & Lower Bound Normalization for IR Evaluation—0
JPLink: On Linking Jobs to Vocational Interest Types—0
Individually Fair Rankings—0
Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions—0
ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking—0
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models—0
A Machine Learning Approach for Smartphone-based Sensing of Roads and Driving Style—0
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