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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

Title	Date	Tasks	Status	Hype
RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank	May 26, 2023	Contrastive LearningLearning-To-Rank	CodeCode Available	1
SELFOOD: Self-Supervised Out-Of-Distribution Detection via Learning to Rank	May 24, 2023	Learning-To-RankOut-of-Distribution Detection	CodeCode Available	0
Learning to Rank Utterances for Query-Focused Meeting Summarization	May 22, 2023	Learning-To-RankMeeting Summarization	—Unverified	0
MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph Fusion	May 20, 2023	DiversityGraph Learning	CodeCode Available	0
Selective Query Processing: a Risk-Sensitive Selection of System Configurations	May 17, 2023	Information RetrievalLearning-To-Rank	—Unverified	0
Unconfounded Propensity Estimation for Unbiased Ranking	May 17, 2023	Learning-To-Rank	—Unverified	0
Efficient and Effective Tree-based and Neural Learning to Rank	May 15, 2023	Information RetrievalLearning-To-Rank	—Unverified	0
THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case Entailment	May 11, 2023	Information RetrievalLearning-To-Rank	CodeCode Available	1
THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained Language Models for Legal Case Retrieval	May 11, 2023	Learning-To-RankRetrieval	CodeCode Available	1
Position Bias Estimation with Item Embedding for Sparse Dataset	May 10, 2023	DenoisingLearning-To-Rank	—Unverified	0

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