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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 701–710 of 753 papers

Title	Date	Tasks	Status
Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs	Nov 2, 2018	Graph RankingKnowledge Graphs	CodeCode Available
Hashing as Tie-Aware Learning to Rank	May 23, 2017	Image RetrievalLearning-To-Rank	CodeCode Available
Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture	Jul 20, 2017	Feature EngineeringLearning-To-Rank	CodeCode Available
Combinatorial Logistic Bandits	Oct 22, 2024	Computational EfficiencyLearning-To-Rank	CodeCode Available
Learning to Rank Rationales for Explainable Recommendation	Jun 10, 2022	Explainable RecommendationLearning-To-Rank	CodeCode Available
Rank Pooling for Action Recognition	Dec 6, 2015	Action RecognitionGesture Recognition	CodeCode Available
Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams	Jan 2, 2019	Learning-To-RankReinforcement Learning	CodeCode Available
Optimization of Decision Tree Evaluation Using SIMD Instructions	May 15, 2022	BinarizationCPU	CodeCode Available
Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank	Jun 25, 2025	Learning-To-Rank	CodeCode Available
Optimizing Group-Fair Plackett-Luce Ranking Models for Relevance and Ex-Post Fairness	Aug 25, 2023	FairnessLearning-To-Rank	CodeCode Available

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