Abstract: Systems and methods are directed to optimizing survey parameters using machine learning. A network system monitors user activity of a plurality of users with respect to an application and provides a notification to users of the plurality of users that satisfy a trigger condition for providing the notification. The network system obtains feedback corresponding to the notification, whereby the feedback indicates whether each of the users accepted, rejected, or ignored the notification. A machine learning model is then trained using input data obtained from the feedback to optimize on one or more parameters used by the network system in providing a future notification. Based on the machine learning model, the future notification is presented to a further set of users using the one or more optimized parameters.

Abstract: Systems and methods are directed to optimizing survey parameters using machine learning. A network system monitors user activity of a plurality of users with respect to an application and provides a notification to users of the plurality of users that satisfy a trigger condition for providing the notification. The network system obtains feedback corresponding to the notification, whereby the feedback indicates whether each of the users accepted, rejected, or ignored the notification. A machine learning model is then trained using input data obtained from the feedback to optimize on one or more parameters used by the network system in providing a future notification. Based on the machine learning model, the future notification is presented to a further set of users using the one or more optimized parameters.