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 351–400 of 753 papers

TitleStatusHype
Individually Fair Rankings—0
Few-Shot Text Ranking with Meta Adapted Synthetic Weak SupervisionCode0
A Frequency-Based Learning-To-Rank Approach for Personal Digital Traces—0
Autoregressive Reasoning over Chains of Facts with TransformersCode0
Weakly Supervised Label SmoothingCode1
Building Cross-Sectional Systematic Strategies By Learning to Rank—0
PiRank: Scalable Learning To Rank via Differentiable SortingCode1
Learning from User Interactions with Rankings: A Unification of the Field—0
Unifying Online and Counterfactual Learning to RankCode1
From Protocol to Screening: A Hybrid Learning Approach for Technology-Assisted Systematic Literature Reviews—0
MatRec: Matrix Factorization for Highly Skewed Dataset—0
Extended Missing Data Imputation via GANs for Ranking Applications—0
What Are You Trying to Do? Semantic Typing of Event Processes—0
U-rank: Utility-oriented Learning to Rank with Implicit Feedback—0
Embedding Meta-Textual Information for Improved Learning to Rank—0
Addressing Purchase-Impression Gap through a Sequential Re-ranker—0
Self-Supervised Ranking for Representation Learning—0
"What Are You Trying to Do?" Semantic Typing of Event Processes—0
Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to RankCode0
Refining Data for Text Generation—0
On the Problem of Underranking in Group-Fair RankingCode0
Learning to Personalize for Web Search Sessions—0
Time-Aware Evidence Ranking for Fact-Checking—0
Learning to Rank under Multinomial Logit Choice—0
PT-Ranking: A Benchmarking Platform for Neural Learning-to-RankCode1
Optimize What You Evaluate With: A Simple Yet Effective Framework For Direct Optimization Of IR Metrics—0
When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to RankCode0
Sample-Rank: Weak Multi-Objective Recommendations Using Rejection Sampling—0
Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to RankCode0
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank SystemsCode1
No-reference Screen Content Image Quality Assessment with Unsupervised Domain Adaptation—0
How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank—0
A Hybrid BERT and LightGBM based Model for Predicting Emotion GIF Categories on Twitter—0
Learning to Rank for Active Learning: A Listwise Approach—0
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation—0
Adversarial Mixture Of Experts with Category Hierarchy Soft ConstraintCode0
Counterfactual Learning to Rank using Heterogeneous Treatment Effect EstimationCode0
Identifying Principals and Accessories in a Complex Case based on the Comprehension of Fact Description—0
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction—0
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model—0
SERank: Optimize Sequencewise Learning to Rank Using Squeeze-and-Excitation NetworkCode1
Learning to Rank Learning Curves—0
Controlling Fairness and Bias in Dynamic Learning-to-RankCode1
Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and WildCode1
Ranking-Incentivized Quality Preserving Content ModificationCode0
Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank—0
L2R2: Leveraging Ranking for Abductive ReasoningCode1
Accelerated Convergence for Counterfactual Learning to RankCode1
Distance-based Positive and Unlabeled Learning for RankingCode0
Context-Aware Learning to Rank with Self-AttentionCode1
Show:102550
← PrevPage 8 of 16Next →

No leaderboard results yet.