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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 526–550 of 753 papers

TitleStatusHype
Learning Visual Features from Snapshots for Web Search—0
Learning what matters - Sampling interesting patterns—0
Leveraging semantically similar queries for ranking via combining representations—0
Leveraging User Behavior History for Personalized Email Search—0
LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs—0
ListBERT: Learning to Rank E-commerce products with Listwise BERT—0
Listwise Learning to Rank with Deep Q-Networks—0
Live Detection of Face Using Machine Learning with Multi-feature Method—0
Local Descriptors Optimized for Average Precision—0
Long Context Modeling with Ranked Memory-Augmented Retrieval—0
Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling—0
Machine Comprehension Based on Learning to Rank—0
Making Better Use of Edges via Perceptual Grouping—0
MarlRank: Multi-agent Reinforced Learning to Rank—0
MatRec: Matrix Factorization for Highly Skewed Dataset—0
MenuAI: Restaurant Food Recommendation System via a Transformer-based Deep Learning Model—0
Metalearners for Ranking Treatment Effects—0
Meta Learning to Rank for Sparsely Supervised Queries—0
Metric-agnostic Ranking Optimization—0
Microsoft AI Challenge India 2018: Learning to Rank Passages for Web Question Answering with Deep Attention Networks—0
MidRank: Learning to rank based on subsequences—0
Minimax Regret for Cascading Bandits—0
Misspecified Linear Bandits—0
Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach—0
Modeling Document Interactions for Learning to Rank with Regularized Self-Attention—0
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