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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 531–540 of 753 papers

TitleStatusHype
CRST: a Claim Retrieval System in Twitter—0
Extractive Headline Generation Based on Learning to Rank for Community Question Answering—0
Live Detection of Face Using Machine Learning with Multi-feature Method—0
A Line in the Sand: Recommendation or Ad-hoc Retrieval?—0
A Collaborative Ranking Model with Multiple Location-based Similarities for Venue Suggestion—0
Towards Non-Parametric Learning to Rank—0
Extreme Learning to Rank via Low Rank Assumption—0
Efficient and Consistent Adversarial Bipartite Matching—0
A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval—0
Biomedical Document Retrieval for Clinical Decision Support System—0
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