Training a machine learning model to determine a predicted time distribution related to electronic communications

Techniques for training a machine learning model to determine a predicted time distribution related to electronic communications are discussed herein. The machine learning model is trained based at least in part on time to open data indicative of respective time to open terms that begin at respectiv...

Full description

Saved in:
Bibliographic Details
Main Authors Zhao, Tong, Tsai, Ming-Chi, Roy Chowdhury, Amber
Format Patent
LanguageEnglish
Published 26.01.2023
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
Online AccessGet full text

Cover

More Information
Summary:Techniques for training a machine learning model to determine a predicted time distribution related to electronic communications are discussed herein. The machine learning model is trained based at least in part on time to open data indicative of respective time to open terms that begin at respective transmission times for electronic communications and end at respective electronic communication access event times. Additionally, based at least in part on the predicted time distribution determined by the machine learning model, respective access scores for an electronic communication being accessed via the user device at the respective times are determined to provide a new electronic communication for rendering via an electronic interface of a user device.
Bibliography:Application Number: US202217881067