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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 471–480 of 753 papers

Title	Date	Tasks	Status
Uncertain Natural Language Inference	Sep 6, 2019	Learning-To-RankNatural Language Inference	—Unverified
Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance	Sep 6, 2019	Learning-To-Rank	CodeCode Available
Answering questions by learning to rank -- Learning to rank by answering questions	Sep 2, 2019	ARCLearning-To-Rank	—Unverified
Explore Entity Embedding Effectiveness in Entity Retrieval	Aug 28, 2019	Entity RetrievalLearning-To-Rank	—Unverified
A Study of BERT for Non-Factoid Question-Answering under Passage Length Constraints	Aug 19, 2019	Learning-To-RankPassage Re-Ranking	—Unverified
A Machine Learning Approach for Smartphone-based Sensing of Roads and Driving Style	Aug 15, 2019	BIG-bench Machine LearningLearning-To-Rank	—Unverified
AIBench: An Industry Standard Internet Service AI Benchmark Suite	Aug 13, 2019	BenchmarkingLearning-To-Rank	—Unverified
Influence of Neighborhood on the Preference of an Item in eCommerce Search	Aug 10, 2019	Learning-To-Rank	—Unverified
FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social Feeds	Aug 8, 2019	Learning-To-Rank	CodeCode Available
Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation	Jul 30, 2019	Decision MakingLearning-To-Rank	CodeCode Available

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