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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 451–475 of 753 papers

TitleStatusHype
Learning Hybrid Representations to Retrieve Semantically Equivalent Questions—0
Learning Minimum Volume Sets and Anomaly Detectors from KNN Graphs—0
Learning Modulo Theories for preference elicitation in hybrid domains—0
Learning More From Less: Towards Strengthening Weak Supervision for Ad-Hoc Retrieval—0
Learning Neural Ranking Models Online from Implicit User Feedback—0
Learning Optimal Card Ranking from Query Reformulation—0
Learning Paraphrasing for Multiword Expressions—0
Learning Rank Functionals: An Empirical Study—0
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation—0
Learning Term Weights for Ad-hoc Retrieval—0
Learning the Peculiar Value of Actions—0
The Amenability Framework: Rethinking Causal Ordering Without Estimating Causal Effects—0
Learning-to-Count by Learning-to-Rank: Weakly Supervised Object Counting & Localization Using Only Pairwise Image Rankings—0
Learning to Differentiate Better from Worse Translations—0
Learning to Exploit Different Translation Resources for Cross Language Information Retrieval—0
Learning to Extract Folktale Keywords—0
Learning to Focus when Ranking Answers—0
Learning to Order Natural Language Texts—0
Learning to Personalize for Web Search Sessions—0
LearningToQuestion at SemEval 2017 Task 3: Ranking Similar Questions by Learning to Rank Using Rich Features—0
Learning to Rank Academic Experts in the DBLP Dataset—0
Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics—0
Learning to Rank Answer Candidates for Automatic Resolution of Crossword Puzzles—0
Learning to Rank based on Analogical Reasoning—0
Learning to Rank Based on Subsequences—0
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