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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 371–380 of 753 papers

Title	Date	Tasks	Status	Hype
On the Problem of Underranking in Group-Fair Ranking	Sep 24, 2020	FairnessLearning-To-Rank	CodeCode Available	0
Learning to Personalize for Web Search Sessions	Sep 17, 2020	Learning-To-RankSession Search	—Unverified	0
Time-Aware Evidence Ranking for Fact-Checking	Sep 10, 2020	ClusteringFact Checking	—Unverified	0
Learning to Rank under Multinomial Logit Choice	Sep 7, 2020	Learning-To-RankPosition	—Unverified	0
PT-Ranking: A Benchmarking Platform for Neural Learning-to-Rank	Aug 31, 2020	BenchmarkingLearning-To-Rank	CodeCode Available	1
Optimize What You Evaluate With: A Simple Yet Effective Framework For Direct Optimization Of IR Metrics	Aug 31, 2020	Information RetrievalLearning-To-Rank	—Unverified	0
When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank	Aug 24, 2020	counterfactualLearning-To-Rank	CodeCode Available	0
Sample-Rank: Weak Multi-Objective Recommendations Using Rejection Sampling	Aug 24, 2020	Learning-To-RankRecommendation Systems	—Unverified	0
Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to Rank	Aug 20, 2020	Information RetrievalLearning-To-Rank	CodeCode Available	0
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems	Aug 19, 2020	Click-Through Rate PredictionLearning-To-Rank	CodeCode Available	1

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