Meta Platforms, Inc.
Utilizing machine learning and composite utility scores from multiple event categories to improve digital content distribution

Abstract:

The present disclosure is directed toward systems, methods, and non-transitory computer readable media for providing digital content to users by applying a machine learning model based on composite utility scores reflecting multiple events categories. For example, the systems described herein can identify, from a digital content publisher, significance ratings of various event categories that a user can perform. The systems can analyze user activities to determine a composite utility score for user based on events that the users have performed. Furthermore, in one or more embodiments, the systems train a machine learning model based on training composite utility scores to identify additional users likely to have elevated composite utility scores. Moreover, the disclosed systems can utilize the trained machine learning model to provide targeted digital content to computing devices of these additional users.