SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–450 of 753 papers

TitleStatusHype
Learning to Rank when Grades Matter—0
Learning-to-Rank with BERT in TF-Ranking—0
Extended Missing Data Imputation via GANs for Ranking Applications—0
Learning-to-Rank with Nested Feedback—0
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model—0
Learning to Rank with Small Set of Ground Truth Data—0
Towards Better Web Search Performance: Pre-training, Fine-tuning and Learning to Rank—0
Learning to Re-rank with Constrained Meta-Optimal Transport—0
Learning to Select: Problem, Solution, and Applications—0
Learning to Temporally Order Medical Events in Clinical Text—0
Learning to Weight Translations using Ordinal Linear Regression and Query-generated Training Data for Ad-hoc Retrieval with Long Queries—0
Learning Translational and Knowledge-based Similarities from Relevance Rankings for Cross-Language Retrieval—0
Learning Visual Features from Snapshots for Web Search—0
Learning what matters - Sampling interesting patterns—0
Answering questions by learning to rank -- Learning to rank by answering questions—0
An IPW-based Unbiased Ranking Metric in Two-sided Markets—0
Leveraging semantically similar queries for ranking via combining representations—0
Towards Constructing Sports News from Live Text Commentary—0
Towards Deep and Representation Learning for Talent Search at LinkedIn—0
Leveraging User Behavior History for Personalized Email Search—0
An Exploratory Study on Simulated Annealing for Feature Selection in Learning-to-Rank—0
LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs—0
A new perspective on classification: optimally allocating limited resources to uncertain tasks—0
Towards Disentangling Relevance and Bias in Unbiased Learning to Rank—0
ListBERT: Learning to Rank E-commerce products with Listwise BERT—0
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 Explainable Test Case Prioritisation with Learning-to-Rank Models—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 More Relevant Product Search Ranking Via Large Language Models: An Empirical Study—0
MatRec: Matrix Factorization for Highly Skewed Dataset—0
Towards Non-Parametric Learning to Rank—0
MenuAI: Restaurant Food Recommendation System via a Transformer-based Deep Learning Model—0
Towards Off-Policy Reinforcement Learning for Ranking Policies with Human Feedback—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 Productionizing Subjective Search Systems—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
Show:102550
← PrevPage 9 of 16Next →

No leaderboard results yet.