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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 276–300 of 753 papers

TitleStatusHype
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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