Amanote Research
Register
Sign In
Interpretable Click-Through Rate Prediction Through Hierarchical Attention
doi 10.1145/3336191.3371785
Full Text
Open PDF
Abstract
Available in
full text
Date
January 20, 2020
Authors
Zeyu Li
Wei Cheng
Yang Chen
Haifeng Chen
Wei Wang
Publisher
ACM
Related search
Estimating Ads’ Click Through Rate With Recurrent Neural Network
ITM Web of Conferences
Expectation and Attention in Hierarchical Auditory Prediction
Journal of Neuroscience
Neuroscience
Predicting Adverse Drug Reactions Through Interpretable Deep Learning Framework
BMC Bioinformatics
Biochemistry
Applied Mathematics
Computer Science Applications
Structural Biology
Molecular Biology
Sifting Through Hierarchical Information
Project Teamwork Assessment and Success Rate Prediction Through Meta-Heuristic Algorithms
Advances in Systems Analysis, Software Engineering, and High Performance Computing
Hierarchical Gated Recurrent Unit With Semantic Attention for Event Prediction
Future Internet
Computer Networks
Communications
Prediction of Radiotherapy Response of Cervical Carcinoma Through Measurement of Proliferation Rate
British Journal of Cancer
Cancer Research
Oncology
Synthesis of Novel Thermoresponsive Glycopolymers Achieved Through Click Chemistry
MRS Bulletin
Materials Science
Theoretical Chemistry
Condensed Matter Physics
Physical
DeepSeqPanII: An Interpretable Recurrent Neural Network Model With Attention Mechanism for Peptide-Hla Class II Binding Prediction