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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 391–400 of 753 papers

TitleStatusHype
Learning to Rank Salient Content for Query-focused Summarization—0
Learning to Rank Scientific Documents from the Crowd—0
Learning to Rank Semantic Coherence for Topic Segmentation—0
Addressing Purchase-Impression Gap through a Sequential Re-ranker—0
Learning to Rank under Multinomial Logit Choice—0
Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval—0
Learning to Rank Utterances for Query-Focused Meeting Summarization—0
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction—0
Learning to Rank Visual Stories From Human Ranking Data—0
Towards Better Web Search Performance: Pre-training, Fine-tuning and Learning to Rank—0
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