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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 576–600 of 753 papers

TitleStatusHype
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
On the Calibration and Uncertainty of Neural Learning to Rank Models for Conversational Search—0
On the Consistency of AUC Pairwise Optimization—0
On the ERM Principle with Networked Data—0
On The Structure of Parametric Tournaments with Application to Ranking from Pairwise Comparisons—0
Ontology-Based Retrieval \& Neural Approaches for BioASQ Ideal Answer Generation—0
OPI at SemEval 2023 Task 1: Image-Text Embeddings and Multimodal Information Retrieval for Visual Word Sense Disambiguation—0
Optimize What You Evaluate With: A Simple Yet Effective Framework For Direct Optimization Of IR Metrics—0
Optimizing Preference Alignment with Differentiable NDCG Ranking—0
Optimizing Ranking Models in an Online Setting—0
Optimizing Ranking Systems Online as Bandits—0
Overview of the CLEF-2019 CheckThat!: Automatic Identification and Verification of Claims—0
Pairwise Judgment Formulation for Semantic Embedding Model in Web Search—0
Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions—0
Par4Sim -- Adaptive Paraphrasing for Text Simplification—0
Pareto Pairwise Ranking for Fairness Enhancement of Recommender Systems—0
Perceptron-like Algorithms and Generalization Bounds for Learning to Rank—0
Perceptron like Algorithms for Online Learning to Rank—0
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