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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 701–710 of 753 papers

TitleStatusHype
Semi-Automatic Construction of Word-Formation Networks (for Polish and Spanish)—0
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
Semi-Supervised Variational Adversarial Active Learning via Learning to Rank and Agreement-Based Pseudo Labeling—0
Sentence-Level Relation Extraction via Contrastive Learning with Descriptive Relation Prompts—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
Separate and Attend in Personal Email Search—0
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