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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 581–590 of 753 papers

TitleStatusHype
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
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