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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 521–530 of 753 papers

Title	Date	Tasks	Status
Retrieve and Re-rank: A Simple and Effective IR Approach to Simple Question Answering over Knowledge Graphs	Nov 1, 2018	Entity LinkingInformation Retrieval	—Unverified
Online Learning to Rank with Features	Oct 5, 2018	Learning-To-Rank	—Unverified
Entity Linking within a Social Media Platform: A Case Study on Yelp	Oct 1, 2018	Entity LinkingLearning-To-Rank	CodeCode Available
Understanding the Gist of Images - Ranking of Concepts for Multimedia Indexing	Sep 23, 2018	Learning-To-Rank	—Unverified
Query Understanding via Entity Attribute Identification	Sep 23, 2018	AttributeLearning-To-Rank	—Unverified
Differentiable Unbiased Online Learning to Rank	Sep 22, 2018	Learning-To-Rank	CodeCode Available
Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System	Sep 19, 2018	Knowledge DistillationLearning-To-Rank	CodeCode Available
Towards Deep and Representation Learning for Talent Search at LinkedIn	Sep 17, 2018	Learning Semantic RepresentationsLearning-To-Rank	—Unverified
Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm	Sep 16, 2018	Learning-To-RankPosition	CodeCode Available
Exact Passive-Aggressive Algorithms for Learning to Rank Using Interval Labels	Aug 18, 2018	Learning-To-Rank	CodeCode Available

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