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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 601–625 of 753 papers

TitleStatusHype
Click-aware purchase prediction with push at the top—0
End-to-End Neural Ad-hoc Ranking with Kernel PoolingCode0
Word-Entity Duet Representations for Document Ranking—0
Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications—0
Hashing as Tie-Aware Learning to RankCode0
Learning to Rank Using Localized Geometric Mean MetricsCode0
Using Titles vs. Full-text as Source for Automated Semantic Document AnnotationCode0
Neural Models for Information Retrieval—0
Transfer Learning by Ranking for Weakly Supervised Object Annotation—0
Misspecified Linear Bandits—0
Improving Pairwise Ranking for Multi-label Image ClassificationCode0
If You Can't Beat Them Join Them: Handcrafted Features Complement Neural Nets for Non-Factoid Answer Reranking—0
Online Learning to Rank in Stochastic Click Models—0
Rank-to-engage: New Listwise Approaches to Maximize Engagement—0
An Attention-Based Deep Net for Learning to Rank—0
Learning what matters - Sampling interesting patterns—0
Simple to Complex Cross-modal Learning to Rank—0
Match-Tensor: a Deep Relevance Model for SearchCode0
Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events—0
Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative StudyCode0
Classification and Learning-to-rank Approaches for Cross-Device Matching at CIKM Cup 2016—0
Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images—0
Neural Attention for Learning to Rank Questions in Community Question Answering—0
Learning to Weight Translations using Ordinal Linear Regression and Query-generated Training Data for Ad-hoc Retrieval with Long Queries—0
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering for WAT 2016—0
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