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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 601–650 of 753 papers

TitleStatusHype
Learning to Rank Retargeted Images—0
autoBagging: Learning to Rank Bagging Workflows with Metalearning—0
Click-aware purchase prediction with push at the top—0
Word-Entity Duet Representations for Document Ranking—0
End-to-End Neural Ad-hoc Ranking with Kernel PoolingCode0
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
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
Addressing Community Question Answering in English and Arabic—0
Enhancing LambdaMART Using Oblivious Trees—0
A Flexible Recommendation System for Cable TV—0
Don't Mention the Shoe! A Learning to Rank Approach to Content Selection for Image Description Generation—0
Online Learning to Rank with Top-k Feedback—0
Computational and Statistical Tradeoffs in Learning to Rank—0
Unbiased Learning-to-Rank with Biased Feedback—0
Bridging the Gap: Incorporating a Semantic Similarity Measure for Effectively Mapping PubMed Queries to Documents—0
Learning to Rank for Synthesizing Planning Heuristics—0
Constrained Multi-Task Learning for Automated Essay Scoring—0
Learning Paraphrasing for Multiword Expressions—0
Towards Constructing Sports News from Live Text Commentary—0
News Citation Recommendation with Implicit and Explicit Semantics—0
DocChat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents—0
Using Learning-To-Rank to Enhance NLM Medical Text Indexer Results—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
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