SYSTEMS AND METHODS FOR NOTIFICATION SEND CONTROL USING NEGATIVE SENTIMENT
Systems, methods, and non-transitory computer readable media are configured to determine a likelihood of a rejection of a notification proposed for delivery to a recipient. A delivery determination for the notification can be performed. Subsequently, the notification can be delivered to the recipient based on the delivery determination.
1 . A computer-implemented method comprising:
determining, by a computing system, a likelihood of a rejection of a notification proposed for delivery to a recipient;
performing, by the computing system, a delivery determination for the notification; and
delivering, by the computing system, the notification to the recipient based on the delivery determination.
2 . The computer-implemented method of claim 1 , further comprising:
determining, by the computing system, a likelihood of a selection of the notification.
3 . The computer-implemented method of claim 1 , wherein the determining the likelihood of the rejection of the notification is based on a machine learning model.
4 . The computer-implemented method of claim 3 , further comprising:
providing, by the computing system, to the machine learning model, feature data for the notification.
5 . The computer-implemented method of claim 3 , further comprising:
providing, by the computing system, to the machine learning model, feature data for the recipient.
6 . The computer-implemented method of claim 3 , further comprising:
providing, by the computing system, to the machine learning model, feature data regarding previous delivery of the notification to the recipient.
7 . The computer-implemented method of claim 1 , wherein the performing the delivery determination for the notification is based at least in part on the likelihood of the rejection of the notification.
8 . The computer-implemented method of claim 7 , wherein the performing the delivery determination for the notification is based at least in part on the likelihood of the selection of the notification.
9 . The computer-implemented method of claim 8 , wherein the performing the delivery determination for the notification is based at least in part on a weight applied to the likelihood of the rejection of the notification.
10 . The computer-implemented method of claim 1 , wherein the performing the delivery determination for the notification comprises:
generating a score based at least in part on the likelihood of the rejection of the notification, a selected weight applied to the likelihood of the rejection of the notification, and a likelihood of a selection of the notification; and
comparing the score to a threshold value.
11 . A system comprising:
at least one processor; and
a memory storing instructions that, when executed by the at least one processor, cause the system to perform:
determining a likelihood of a rejection of a notification proposed for delivery to a recipient;
performing a delivery determination for the notification; and
delivering the notification to the recipient based on the delivery determination.
12 . The system of claim 11 , wherein the instructions, when executed by the at least one processor, further cause the system to perform:
determining a likelihood of a selection of the notification.
13 . The system of claim 11 , wherein the determining the likelihood of the rejection of the notification is based on a machine learning model.
14 . The system of claim 11 , wherein the performing the delivery determination for the notification is based at least in part on the likelihood of the rejection of the notification.
15 . The system of claim 11 , wherein the performing the delivery determination for the notification comprises:
generating a score based at least in part on the likelihood of the rejection of the notification, a selected weight applied to the likelihood of the rejection of the notification, and a likelihood of a selection of the notification; and
comparing the score to a threshold value.
16 . A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising:
determining a likelihood of a rejection of a notification proposed for delivery to a recipient;
performing a delivery determination for the notification; and
delivering the notification to the recipient based on the delivery determination.
17 . The non-transitory computer-readable storage medium of claim 16 , wherein the instructions, when executed by the at least one processor of the computing system, further cause the computing system to perform:
determining a likelihood of a selection of the notification.
18 . The non-transitory computer-readable storage medium of claim 16 , wherein the determining the likelihood of the rejection of the notification is based on a machine learning model.
19 . The non-transitory computer-readable storage medium of claim 16 , wherein the performing the delivery determination for the notification is based at least in part on the likelihood of the rejection of the notification.
20 . The non-transitory computer-readable storage medium of claim 16 , wherein the performing the delivery determination for the notification comprises:
generating a score based at least in part on the likelihood of the rejection of the notification, a selected weight applied to the likelihood of the rejection of the notification, and a likelihood of a selection of the notification; and
comparing the score to a threshold value.