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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

TitleStatusHype
Addressing Community Question Answering in English and Arabic—0
Enhancing LambdaMART Using Oblivious Trees—0
A Flexible Recommendation System for Cable TV—0
Don't Mention the Shoe! A Learning to Rank Approach to Content Selection for Image Description Generation—0
Online Learning to Rank with Top-k Feedback—0
Computational and Statistical Tradeoffs in Learning to Rank—0
Unbiased Learning-to-Rank with Biased Feedback—0
Bridging the Gap: Incorporating a Semantic Similarity Measure for Effectively Mapping PubMed Queries to Documents—0
Learning to Rank for Synthesizing Planning Heuristics—0
Constrained Multi-Task Learning for Automated Essay Scoring—0
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