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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 161–170 of 753 papers

TitleStatusHype
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs—0
Position Bias Estimation for Unbiased Learning-to-Rank in eCommerce Search—0
Direct Learning to Rank and Rerank—0
Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events—0
Analysis of Regression Tree Fitting Algorithms in Learning to Rank—0
DocChat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents—0
Don't Just Pay Attention, PLANT It: Transfer L2R Models to Fine-tune Attention in Extreme Multi-Label Text Classification—0
Don't Mention the Shoe! A Learning to Rank Approach to Content Selection for Image Description Generation—0
BanditRank: Learning to Rank Using Contextual Bandits—0
Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity—0
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