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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

Showing 651700 of 753 papers

TitleStatusHype
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering for WAT 20160
Chiplet Placement Order Exploration Based on Learning to Rank with Graph Representation0
Choice by Elimination via Deep Neural Networks0
CICBUAPnlp: Graph-Based Approach for Answer Selection in Community Question Answering Task0
Classification and Learning-to-rank Approaches for Cross-Device Matching at CIKM Cup 20160
Click-aware purchase prediction with push at the top0
Coarse-to-Fine Contrastive Learning on Graphs0
Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context0
Using Learning-To-Rank to Enhance NLM Medical Text Indexer Results0
Communication-Efficient Algorithms for Statistical Optimization0
Community-based Cyberreading for Information Understanding0
Compound virtual screening by learning-to-rank with gradient boosting decision tree and enrichment-based cumulative gain0
Computational and Statistical Tradeoffs in Learning to Rank0
Consistent Position Bias Estimation without Online Interventions for Learning-to-Rank0
Constrained Multi-Task Learning for Automated Essay Scoring0
Content-Based Features to Rank Influential Hidden Services of the Tor Darknet0
Content Selection for Real-time Sports News Construction from Commentary Texts0
Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events0
Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rank0
Selective Query Processing: a Risk-Sensitive Selection of System Configurations0
Bag-of-Words Forced Decoding for Cross-Lingual Information Retrieval0
Convolutional Neural Networks for Soft Matching N-Grams in Ad-hoc Search0
Convolutional Neural Networks vs. Convolution Kernels: Feature Engineering for Answer Sentence Reranking0
Correcting for Selection Bias in Learning-to-rank Systems0
A Hierarchical Semantics-Aware Distributional Similarity Scheme0
Cost-Sensitive Feature-Value Acquisition Using Feature Relevance0
Self-Attentive Document Interaction Networks for Permutation Equivariant Ranking0
A General Framework for Counterfactual Learning-to-Rank0
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion0
Word-Entity Duet Representations for Document Ranking0
Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network0
Cross-Lingual Learning-to-Rank with Shared Representations0
Cross-lingual Subjectivity Detection for Resource Lean Languages0
CRST: a Claim Retrieval System in Twitter0
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer0
Self-Supervised Ranking for Representation Learning0
Baby Bear: Seeking a Just Right Rating Scale for Scalar Annotations0
De-Biased Modelling of Search Click Behavior with Reinforcement Learning0
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data0
Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity0
Deep Domain Specialisation for single-model multi-domain learning to rank0
Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images0
Deep Multi-view Learning to Rank0
Deep Neural Network for Learning to Rank Query-Text Pairs0
Deep Pairwise Learning To Rank For Search Autocomplete0
Deep Ranking Ensembles for Hyperparameter Optimization0
Deep Ranking for Person Re-identification via Joint Representation Learning0
Detect2Rank : Combining Object Detectors Using Learning to Rank0
Semantic Relatedness of Wikipedia Concepts -- Benchmark Data and a Working Solution0
Dialog Generation Using Multi-Turn Reasoning Neural Networks0
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