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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

Showing 671680 of 753 papers

TitleStatusHype
Entity Linking within a Social Media Platform: A Case Study on YelpCode0
Using clarification questions to improve software developers' Web searchCode0
Learning to rank for censored survival dataCode0
CoSPLADE: Contextualizing SPLADE for Conversational Information RetrievalCode0
Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender SystemCode0
Contextual Semibandits via Supervised Learning OraclesCode0
End-to-End Neural Ad-hoc Ranking with Kernel PoolingCode0
On the Problem of Underranking in Group-Fair RankingCode0
Off-policy evaluation for slate recommendationCode0
Ranking for Relevance and Display Preferences in Complex Presentation LayoutsCode0
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