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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 426–450 of 753 papers

TitleStatusHype
A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval—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
Towards More Relevant Product Search Ranking Via Large Language Models: An Empirical Study—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
Towards Non-Parametric Learning to Rank—0
MatRec: Matrix Factorization for Highly Skewed Dataset—0
Towards Off-Policy Reinforcement Learning for Ranking Policies with Human Feedback—0
MenuAI: Restaurant Food Recommendation System via a Transformer-based Deep Learning Model—0
Towards Productionizing Subjective Search Systems—0
Metalearners for Ranking Treatment Effects—0
Meta Learning to Rank for Sparsely Supervised Queries—0
A Network Framework for Noisy Label Aggregation in Social Media—0
Metric-agnostic Ranking Optimization—0
Towards Theoretical Understanding of Weak Supervision for Information Retrieval—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
An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking—0
Misspecified Linear Bandits—0
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