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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 651–700 of 753 papers

TitleStatusHype
Convolutional Neural Networks vs. Convolution Kernels: Feature Engineering for Answer Sentence Reranking—0
Learning to Rank Personalized Search Results in Professional Networks—0
Off-policy evaluation for slate recommendationCode0
Machine Comprehension Based on Learning to Rank—0
Transfer-Based Learning-to-Rank Assessment of Medical Term Technicality—0
Online Learning to Rank with Feedback at the Top—0
Generalization error bounds for learning to rank: Does the length of document lists matter?—0
Choice by Elimination via Deep Neural Networks—0
DCM Bandits: Learning to Rank with Multiple ClicksCode0
Learning Minimum Volume Sets and Anomaly Detectors from KNN Graphs—0
TermPicker: Enabling the Reuse of Vocabulary Terms by Exploiting Data from the Linked Open Data Cloud - An Extended Technical Report—0
Rank Pooling for Action RecognitionCode0
Learning to Rank Based on Subsequences—0
Activity Auto-Completion: Predicting Human Activities From Partial Videos—0
Predtron: A Family of Online Algorithms for General Prediction Problems—0
MidRank: Learning to rank based on subsequences—0
Images Don't Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank—0
Handling Class Imbalance in Link Prediction using Learning to Rank Techniques—0
Factorizing LambdaMART for cold start recommendations—0
Stochastic Top-k ListNet—0
More Accurate Question Answering on FreebaseCode0
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering forWAT 2015—0
Inducing Clause-Combining Rules: A Case Study with the SPaRKy Restaurant Corpus—0
BEER 1.1: ILLC UvA submission to metrics and tuning taskCode0
LDTM: A Latent Document Type Model for Cumulative Citation Recommendation—0
Learning Modulo Theories for preference elicitation in hybrid domains—0
Perceptron like Algorithms for Online Learning to Rank—0
Challenges in clinical natural language processing for automated disorder normalization—0
FAQ-based Question Answering via Word Alignment—0
Non-convex Regularizations for Feature Selection in Ranking With Sparse SVM—0
Learning Hybrid Representations to Retrieve Semantically Equivalent Questions—0
SACRY: Syntax-based Automatic Crossword puzzle Resolution sYstem—0
Learning to Explain Entity Relationships in Knowledge GraphsCode0
SOLAR: Scalable Online Learning Algorithms for Ranking—0
Bring you to the past: Automatic Generation of Topically Relevant Event Chronicles—0
CICBUAPnlp: Graph-Based Approach for Answer Selection in Community Question Answering Task—0
Making Better Use of Edges via Perceptual Grouping—0
Similarity Learning on an Explicit Polynomial Kernel Feature Map for Person Re-Identification—0
Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network—0
Deep Ranking for Person Re-identification via Joint Representation Learning—0
Bag-of-Words Forced Decoding for Cross-Lingual Information Retrieval—0
Fast and Accurate Preordering for SMT using Neural Networks—0
Learning to rank in person re-identification with metric ensembles—0
Contextual Semibandits via Supervised Learning OraclesCode0
Cascading Bandits: Learning to Rank in the Cascade Model—0
Learning Efficient Anomaly Detectors from K-NN Graphs—0
Regression and Learning to Rank Aggregation for User Engagement Evaluation—0
Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels—0
Learning to Rank Academic Experts in the DBLP Dataset—0
Detect2Rank : Combining Object Detectors Using Learning to Rank—0
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