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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 651–675 of 753 papers

TitleStatusHype
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering for WAT 2016—0
Chiplet Placement Order Exploration Based on Learning to Rank with Graph Representation—0
Choice by Elimination via Deep Neural Networks—0
CICBUAPnlp: Graph-Based Approach for Answer Selection in Community Question Answering Task—0
Classification and Learning-to-rank Approaches for Cross-Device Matching at CIKM Cup 2016—0
Click-aware purchase prediction with push at the top—0
Coarse-to-Fine Contrastive Learning on Graphs—0
Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context—0
Using Learning-To-Rank to Enhance NLM Medical Text Indexer Results—0
Communication-Efficient Algorithms for Statistical Optimization—0
Community-based Cyberreading for Information Understanding—0
Compound virtual screening by learning-to-rank with gradient boosting decision tree and enrichment-based cumulative gain—0
Computational and Statistical Tradeoffs in Learning to Rank—0
Consistent Position Bias Estimation without Online Interventions for Learning-to-Rank—0
Constrained Multi-Task Learning for Automated Essay Scoring—0
Content-Based Features to Rank Influential Hidden Services of the Tor Darknet—0
Content Selection for Real-time Sports News Construction from Commentary Texts—0
Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events—0
Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rank—0
Selective Query Processing: a Risk-Sensitive Selection of System Configurations—0
Bag-of-Words Forced Decoding for Cross-Lingual Information Retrieval—0
Convolutional Neural Networks for Soft Matching N-Grams in Ad-hoc Search—0
Convolutional Neural Networks vs. Convolution Kernels: Feature Engineering for Answer Sentence Reranking—0
Correcting for Selection Bias in Learning-to-rank Systems—0
A Hierarchical Semantics-Aware Distributional Similarity Scheme—0
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