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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 251–300 of 753 papers

TitleStatusHype
MenuAI: Restaurant Food Recommendation System via a Transformer-based Deep Learning Model—0
SML:Enhance the Network Smoothness with Skip Meta Logit for CTR Prediction—0
Toward Understanding Privileged Features Distillation in Learning-to-Rank—0
Learning To Rank Diversely At Airbnb—0
Joint Upper & Lower Bound Normalization for IR Evaluation—0
ImitAL: Learned Active Learning Strategy on Synthetic DataCode0
Intersection of Parallels as an Early Stopping CriterionCode0
Reinforcement Learning to Rank with Coarse-grained Labels—0
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data—0
Image Quality Assessment: Learning to Rank Image Distortion Level—0
Noise tolerance of learning to rank under class-conditional label noise—0
Using clarification questions to improve software developers' Web searchCode0
What makes you change your mind? An empirical investigation in online group decision-making conversations—0
Model-based Unbiased Learning to RankCode0
A General Framework for Pairwise Unbiased Learning to RankCode0
Simultaneously Learning Stochastic and Adversarial Bandits under the Position-Based Model—0
Multi-Label Learning to Rank through Multi-Objective Optimization—0
Recommendation Systems with Distribution-Free Reliability Guarantees—0
Learning to Rank with Small Set of Ground Truth Data—0
On Curriculum Learning for Commonsense ReasoningCode0
Using clarification questions to improve software developers’ Web searchCode0
ListBERT: Learning to Rank E-commerce products with Listwise BERT—0
The Amenability Framework: Rethinking Causal Ordering Without Estimating Causal Effects—0
Reaching the End of Unbiasedness: Uncovering Implicit Limitations of Click-Based Learning to Rank—0
Efficient and Accurate Top-K Recovery from Choice Data—0
FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank—0
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback—0
Learning to Rank Rationales for Explainable RecommendationCode0
Offline Evaluation of Ranked Lists using Parametric Estimation of Propensities—0
Pessimistic Off-Policy Optimization for Learning to Rank—0
Scalar is Not Enough: Vectorization-based Unbiased Learning to RankCode0
Glance to Count: Learning to Rank with Anchors for Weakly-supervised Crowd Counting—0
A Simple yet Effective Framework for Active Learning to Rank—0
Optimization of Decision Tree Evaluation Using SIMD InstructionsCode0
Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling—0
Compound virtual screening by learning-to-rank with gradient boosting decision tree and enrichment-based cumulative gain—0
Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency—0
Learning to Rank Visual Stories From Human Ranking DataCode0
Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in RankingCode0
MovieMat: Context-aware Movie Recommendation with Matrix Factorization by Matrix Fitting—0
Groupwise Query Performance Prediction with BERTCode0
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion—0
Is Non-IID Data a Threat in Federated Online Learning to Rank?Code0
Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank—0
Which Tricks Are Important for Learning to Rank?—0
Unbiased Top-k Learning to Rank with Causal Likelihood DecompositionCode0
Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to RankCode0
Minimax Regret for Cascading Bandits—0
Personalized Execution Time Optimization for the Scheduled Jobs—0
Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths—0
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