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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 571–580 of 753 papers

Title	Date	Tasks	Status
A Hybrid BERT and LightGBM based Model for Predicting Emotion GIF Categories on Twitter	Aug 14, 2020	Learning-To-Rank	—Unverified
Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality	Jul 29, 2021	Learning-To-RankRecommendation Systems	—Unverified
Ranking Facts for Explaining Answers to Elementary Science Questions	Oct 18, 2021	Interpretable Machine LearningLearning-To-Rank	—Unverified
Understanding the Effects of the Baidu-ULTR Logging Policy on Two-Tower Models	Sep 18, 2024	Learning-To-Rank	—Unverified
Understanding the Gist of Images - Ranking of Concepts for Multimedia Indexing	Sep 23, 2018	Learning-To-Rank	—Unverified
Understanding User Behavior in Carousel Recommendation Systems for Click Modeling and Learning to Rank	Jul 4, 2023	Information RetrievalLearning-To-Rank	—Unverified
Ranking Kernels for Structures and Embeddings: A Hybrid Preference and Classification Model	Sep 1, 2017	Community Question AnsweringGeneral Classification	—Unverified
Ranking Measures and Loss Functions in Learning to Rank	Dec 1, 2009	General ClassificationLearning-To-Rank	—Unverified
Ranking & Reweighting Improves Group Distributional Robustness	May 9, 2023	Information RetrievalLearning-To-Rank	—Unverified
Ranking Robustness Under Adversarial Document Manipulations	Jun 13, 2018	Learning-To-RankRetrieval	—Unverified

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