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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 451–460 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
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