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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 401–450 of 753 papers

TitleStatusHype
How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank—0
A Hybrid BERT and LightGBM based Model for Predicting Emotion GIF Categories on Twitter—0
Learning to Rank for Active Learning: A Listwise Approach—0
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation—0
Adversarial Mixture Of Experts with Category Hierarchy Soft ConstraintCode0
Counterfactual Learning to Rank using Heterogeneous Treatment Effect EstimationCode0
Identifying Principals and Accessories in a Complex Case based on the Comprehension of Fact Description—0
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction—0
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model—0
Learning to Rank Learning Curves—0
Ranking-Incentivized Quality Preserving Content ModificationCode0
Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank—0
Distance-based Positive and Unlabeled Learning for RankingCode0
Policy-Aware Unbiased Learning to Rank for Top-k RankingsCode0
Learning to rank music tracks using triplet loss—0
Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction—0
Modeling Document Interactions for Learning to Rank with Regularized Self-Attention—0
Interpretable Learning-to-Rank with Generalized Additive Models—0
Query-level Early Exit for Additive Learning-to-Rank Ensembles—0
Learning to Rank Intents in Voice Assistants—0
Valid Explanations for Learning to Rank Models—0
Unbiased Learning to Rank: Online or Offline?—0
Fast and Memory-Efficient Neural Code Completion—0
Learning to Rank in the Position Based Model with Bandit Feedback—0
Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions—0
Learning-to-Rank with BERT in TF-Ranking—0
Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph—0
The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection—0
A Recurrent Model for Collective Entity Linking with Adaptive FeaturesCode0
Towards Productionizing Subjective Search Systems—0
AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online—0
Identifying Notable News Stories—0
StochasticRank: Global Optimization of Scale-Free Discrete Functions—0
Handling Position Bias for Unbiased Learning to Rank in Hotels Search—0
Cognitive Biomarker Prioritization in Alzheimer's Disease using Brain Morphometric Data—0
Learning to rank for uplift modeling—0
Listwise Learning to Rank with Deep Q-Networks—0
AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment—0
Eliminating Search Intent Bias in Learning to Rank—0
JPLink: On Linking Jobs to Vocational Interest Types—0
Boosting API Recommendation with Implicit Feedback—0
Safe Exploration for Optimizing Contextual BanditsCode0
Correcting for Selection Bias in Learning-to-rank Systems—0
TopRank+: A Refinement of TopRank Algorithm—0
Influence Diagram Bandits—0
Cost-Sensitive Feature-Value Acquisition Using Feature Relevance—0
SetRank: Learning a Permutation-Invariant Ranking Model for Information RetrievalCode0
Duet at TREC 2019 Deep Learning TrackCode0
Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity—0
An Alternative Cross Entropy Loss for Learning-to-Rank—0
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