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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 301–310 of 753 papers

TitleStatusHype
An Exploratory Study on Simulated Annealing for Feature Selection in Learning-to-Rank—0
A Hybrid BERT and LightGBM based Model for Predicting Emotion GIF Categories on Twitter—0
Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths—0
Bridging the Gap: Incorporating a Semantic Similarity Measure for Effectively Mapping PubMed Queries to Documents—0
Estimating Position Bias without Intrusive Interventions—0
A new perspective on classification: optimally allocating limited resources to uncertain tasks—0
Entailment-Preserving First-order Logic Representations in Natural Language Entailment—0
Bounded-Abstention Pairwise Learning to Rank—0
Ensemble Ranking Model with Multiple Pretraining Strategies for Web Search—0
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning—0
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