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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 151–160 of 753 papers

Title	Date	Tasks	Status	Score
Learning to Explain Entity Relationships in Knowledge Graphs	Jul 1, 2015	Knowledge GraphsLearning-To-Rank	CodeCode Available	5
Fitting Sentence Level Translation Evaluation with Many Dense Features	Oct 1, 2014	Learning-To-RankMachine Translation	CodeCode Available	5
FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social Feeds	Aug 8, 2019	Learning-To-Rank	CodeCode Available	5
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources	Nov 6, 2017	Context Aware Product RecommendationLearning-To-Rank	CodeCode Available	5
Counterfactual Learning to Rank using Heterogeneous Treatment Effect Estimation	Jul 19, 2020	counterfactualHeterogeneous Treatment Effect Estimation	CodeCode Available	5
LaSER: Language-Specific Event Recommendation	Feb 24, 2023	Information RetrievalLearning-To-Rank	CodeCode Available	5
Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue Systems	Feb 20, 2023	Learning-To-RankReinforcement Learning (RL)	CodeCode Available	5
Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank	Oct 7, 2020	Learning-To-RankSentence	CodeCode Available	5
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMs	Feb 19, 2024	Document RankingLearning-To-Rank	CodeCode Available	5
Exact Passive-Aggressive Algorithms for Learning to Rank Using Interval Labels	Aug 18, 2018	Learning-To-Rank	CodeCode Available	5

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