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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 621–630 of 753 papers

TitleStatusHype
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
VSoLSCSum: Building a Vietnamese Sentence-Comment Dataset for Social Context Summarization—0
Quad-networks: unsupervised learning to rank for interest point detection—0
Learning to Rank Scientific Documents from the Crowd—0
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