UTILIZING MACHINE LEARNING MODELS TO GENERATE PREDICTED REFEREE INTERACTION METRICS FOR GENERATING AND TRANSMITTING DIGITAL NOTIFICATIONS ACROSS COMPUTER NETWORKS TO REFERRER CLIENT DEVICES

Abstract

The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning to generate predicted referee interaction metrics for building a digital notification distribution policy for tiers of referrer client devices and transmitting digital notifications to referrer client devices across computer networks. In particular, in one or more embodiments, the disclosed systems utilize a referee interaction prediction machine learning model that generate predicted referee interaction metrics indicating likelihoods of downstream interactions of referee client devices based on features of referrer client devices. The disclosed systems generate referrer client device tiers for referrer client devices based on the predicted referee interaction metrics and then utilizes an optimization model to generate a digital notification distribution policy for the tiers of the referrer client devices. Further, the disclosed systems transmit digital notifications to referrer client devices in accordance with the digital notification policy and the referrer client device tiers.

CPC Classifications