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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 501–525 of 753 papers

TitleStatusHype
Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition—0
Learning to Rank Personalized Search Results in Professional Networks—0
Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks—0
Learning to Rank Proposals for Object Detection—0
Learning to rank quantum circuits for hardware-optimized performance enhancement—0
Learning to Rank Question Answer Pairs with Bilateral Contrastive Data Augmentation—0
Learning To Rank Resources with GNN—0
Learning to Rank Retargeted Images—0
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
Learning to Rank under Multinomial Logit Choice—0
Learning to Rank Utterances for Query-Focused Meeting Summarization—0
Learning to Rank Visual Stories From Human Ranking Data—0
Learning to Rank when Grades Matter—0
Learning-to-Rank with BERT in TF-Ranking—0
Extended Missing Data Imputation via GANs for Ranking Applications—0
Learning-to-Rank with Nested Feedback—0
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model—0
Learning to Rank with Small Set of Ground Truth Data—0
Learning to Re-rank with Constrained Meta-Optimal Transport—0
Learning to Select: Problem, Solution, and Applications—0
Learning to Temporally Order Medical Events in Clinical Text—0
Learning to Weight Translations using Ordinal Linear Regression and Query-generated Training Data for Ad-hoc Retrieval with Long Queries—0
Learning Translational and Knowledge-based Similarities from Relevance Rankings for Cross-Language Retrieval—0
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