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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 551–560 of 753 papers

TitleStatusHype
Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm—0
Model Spider: Learning to Rank Pre-Trained Models Efficiently—0
MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search—0
MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank—0
MovieMat: Context-aware Movie Recommendation with Matrix Factorization by Matrix Fitting—0
MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model—0
MTE-NN at SemEval-2016 Task 3: Can Machine Translation Evaluation Help Community Question Answering?—0
Multi-Label Learning to Rank through Multi-Objective Optimization—0
Multi-objective Learning to Rank by Model Distillation—0
Multi-Task Off-Policy Learning from Bandit Feedback—0
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