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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 141–150 of 753 papers

TitleStatusHype
DocChat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents—0
An Attention-Based Deep Net for Learning to Rank—0
BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale—0
A Frequency-Based Learning-To-Rank Approach for Personal Digital Traces—0
BanditRank: Learning to Rank Using Contextual Bandits—0
Bandit Learning to Rank with Position-Based Click Models: Personalized and Equal Treatments—0
An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems—0
Position Bias Estimation for Unbiased Learning-to-Rank in eCommerce Search—0
Analysis of Regression Tree Fitting Algorithms in Learning to Rank—0
Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events—0
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