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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 641–650 of 753 papers

TitleStatusHype
News Citation Recommendation with Implicit and Explicit Semantics—0
Constrained Multi-Task Learning for Automated Essay Scoring—0
DocChat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents—0
Towards Constructing Sports News from Live Text Commentary—0
Learning Optimal Card Ranking from Query Reformulation—0
Learning Term Weights for Ad-hoc Retrieval—0
Recognizing Reference Spans and Classifying their Discourse Facets—0
ECNU at SemEval-2016 Task 7: An Enhanced Supervised Learning Method for Lexicon Sentiment Intensity Ranking—0
QU-IR at SemEval 2016 Task 3: Learning to Rank on Arabic Community Question Answering Forums with Word Embedding—0
MTE-NN at SemEval-2016 Task 3: Can Machine Translation Evaluation Help Community Question Answering?—0
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