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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 631–640 of 753 papers

Title	Date	Tasks	Status	Hype
A Flexible Recommendation System for Cable TV	Sep 13, 2016	DiversityLearning-To-Rank	—Unverified	0
Don't Mention the Shoe! A Learning to Rank Approach to Content Selection for Image Description Generation	Sep 1, 2016	Image DescriptionImage Retrieval	—Unverified	0
Learning Latent Vector Spaces for Product Search	Aug 25, 2016	Learning-To-Rankparameter estimation	CodeCode Available	1
Online Learning to Rank with Top-k Feedback	Aug 23, 2016	Learning-To-Rank	—Unverified	0
Computational and Statistical Tradeoffs in Learning to Rank	Aug 22, 2016	Learning-To-Rank	—Unverified	0
Unbiased Learning-to-Rank with Biased Feedback	Aug 16, 2016	counterfactualCounterfactual Inference	—Unverified	0
Bridging the Gap: Incorporating a Semantic Similarity Measure for Effectively Mapping PubMed Queries to Documents	Aug 5, 2016	Information RetrievalLearning-To-Rank	—Unverified	0
Learning to Rank for Synthesizing Planning Heuristics	Aug 3, 2016	Learning-To-Rankregression	—Unverified	0
Using Learning-To-Rank to Enhance NLM Medical Text Indexer Results	Aug 1, 2016	Learning-To-Rank	—Unverified	0
Learning Paraphrasing for Multiword Expressions	Aug 1, 2016	Learning-To-RankMachine Translation	—Unverified	0

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