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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 76–100 of 753 papers

TitleStatusHype
A Learning-to-Rank Approach for Image Color Enhancement—0
Chiplet Placement Order Exploration Based on Learning to Rank with Graph Representation—0
Application of the Ranking Relative Principal Component Attributes Network Model (REL-PCANet) for the Inclusive Development Index Estimation—0
Addressing Purchase-Impression Gap through a Sequential Re-ranker—0
Choice by Elimination via Deep Neural Networks—0
CICBUAPnlp: Graph-Based Approach for Answer Selection in Community Question Answering Task—0
A Passage-Based Approach to Learning to Rank Documents—0
Answering questions by learning to rank - Learning to rank by answering questions—0
A Knowledge Graph Based Solution for Entity Discovery and Linking in Open-Domain Questions—0
Answering questions by learning to rank -- Learning to rank by answering questions—0
AIBench: An Industry Standard Internet Service AI Benchmark Suite—0
Addressing Community Question Answering in English and Arabic—0
Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems—0
An IPW-based Unbiased Ranking Metric in Two-sided Markets—0
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
A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval—0
Bounded-Abstention Pairwise Learning to Rank—0
Improving Neural Ranking via Lossless Knowledge Distillation—0
A Hierarchical Semantics-Aware Distributional Similarity Scheme—0
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering forWAT 2015—0
A new perspective on classification: optimally allocating limited resources to uncertain tasks—0
Bridging the Gap: Incorporating a Semantic Similarity Measure for Effectively Mapping PubMed Queries to Documents—0
Bring you to the past: Automatic Generation of Topically Relevant Event Chronicles—0
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering for WAT 2016—0
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