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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 451–500 of 753 papers

TitleStatusHype
An Attention-Based Deep Net for Learning to Rank—0
Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach—0
Towards Two-Stage Counterfactual Learning to Rank—0
Toward Understanding Privileged Features Distillation in Learning-to-Rank—0
Transfer-Based Learning-to-Rank Assessment of Medical Term Technicality—0
Transfer Learning by Ranking for Weakly Supervised Object Annotation—0
Modeling Document Interactions for Learning to Rank with Regularized Self-Attention—0
Addressing Community Question Answering in English and Arabic—0
Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm—0
An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems—0
Model Spider: Learning to Rank Pre-Trained Models Efficiently—0
MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search—0
MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank—0
TRIVEA: Transparent Ranking Interpretation using Visual Explanation of Black-Box Algorithmic Rankers—0
MovieMat: Context-aware Movie Recommendation with Matrix Factorization by Matrix Fitting—0
MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model—0
MTE-NN at SemEval-2016 Task 3: Can Machine Translation Evaluation Help Community Question Answering?—0
Multi-Label Learning to Rank through Multi-Objective Optimization—0
Multi-objective Learning to Rank by Model Distillation—0
Multi-Task Off-Policy Learning from Bandit Feedback—0
Multivariate Spearman's rho for aggregating ranks using copulas—0
Neural Attention for Learning to Rank Questions in Community Question Answering—0
Neural Feature Selection for Learning to Rank—0
Neural Models for Information Retrieval—0
Analysis of Regression Tree Fitting Algorithms in Learning to Rank—0
Neural Rankers are hitherto Outperformed by Gradient Boosted Decision Trees—0
Neural Ranking Models with Multiple Document Fields—0
Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems—0
News Citation Recommendation with Implicit and Explicit Semantics—0
Noise tolerance of learning to rank under class-conditional label noise—0
Non-convex Regularizations for Feature Selection in Ranking With Sparse SVM—0
Analysis of E-commerce Ranking Signals via Signal Temporal Logic—0
No-reference Screen Content Image Quality Assessment with Unsupervised Domain Adaptation—0
NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models—0
Offline Evaluation of Ranked Lists using Parametric Estimation of Propensities—0
Offline Learning for Combinatorial Multi-armed Bandits—0
An Alternative Cross Entropy Loss for Learning-to-Rank—0
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model—0
Two-Layer Generalization Analysis for Ranking Using Rademacher Average—0
On Application of Learning to Rank for E-Commerce Search—0
ULTRA: An Unbiased Learning To Rank Algorithm Toolbox—0
On Learning to Rank Long Sequences with Contextual Bandits—0
Online Diverse Learning to Rank from Partial-Click Feedback—0
Online Learning of Optimally Diverse Rankings—0
Online Learning to Rank in Stochastic Click Models—0
Online Learning to Rank with Features—0
Online Learning to Rank with Feedback at the Top—0
Online Learning to Rank with Top-k Feedback—0
On Lipschitz Continuity and Smoothness of Loss Functions in Learning to Rank—0
A Multi-Perspective Learning to Rank Approach to Support Children's Information Seeking in the Classroom—0
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