Automatic recipient targeting for notifications
View Patent ↗A method may include one or more computing devices receiving an indication that a triggering action has been detected from a client device, identifying one or more notifications associated with the triggering action, wherein the one or more notifications have been stored in a queue prior to receiving the indication that the triggering action has been detected, and, for each of the one or more identified notifications, calculating a click-through probability that a user associated with the client device will interact with the notification, wherein the click-through probability is calculated based at least in part on a period the notification was stored in the queue prior to receiving the indication that the of the triggering action has been detected, and determining whether the calculated click-through probability satisfies a threshold and sending, in response to determining that the calculated click-through probability satisfies the threshold, the identified notification to the client device.
1. A method comprising, by one or more computing devices:
receiving an indication that a triggering action has been detected from a client device;
identifying one or more notifications associated with the triggering action, wherein the one or more notifications have been stored in a queue prior to receiving the indication that the triggering action has been detected; and
for each of the one or more identified notifications:
calculating a click-through probability that a user associated with the client device will interact with the notification, wherein the click-through probability is calculated based at least in part on a period the notification was stored in the queue prior to receiving the indication that the of the triggering action has been detected;
determining whether the calculated click-through probability satisfies a threshold; and
sending, in response to determining that the calculated click-through probability satisfies the threshold, the identified notification to the client device.
2. The method of claim 1 , wherein the triggering action comprises:
a click-through of an icon;
a mouseover of an icon; or
a request to log in to an online-networking system.
3. The method of claim 1 , wherein, for each of the one or more identified notifications, the click-through probability is calculated further based on a machine-learning model.
4. The method of claim 3 , further comprising:
detecting one or more actions taken by the user upon display of one or more of the identified notifications; and
modifying the machine-learning model based on the detected actions.
5. The method of claim 1 , wherein one or more of the identified notifications are associated with an application, and wherein the click-through probability for each notification associated with the application is calculated further based on:
the user's frequency of interacting with the application;
the user's click-through rate for one or more notifications associated with the application that were previously sent to the client device;
a time of the user's most recent interaction with the application; or
a category corresponding to the application.
6. The method of claim 1 , wherein one or more of the identified notifications are associated with an application, and wherein the click-through probability for each notification associated with the application is calculated further based on:
a number of daily active users of the application;
a number of monthly active users of the application;
a growth rate of the application's number of daily active users;
a growth rate of the application's number of monthly active users;
a conversion rate associated with users of the application fitting a particular demographic description; or
a language supported by the application.
7. The method of claim 1 , wherein one or more of the identified notifications are associated with an application, and wherein the click-through probability for each notification associated with the application is calculated further based on:
the user's interactions with one or more other applications similar to the application.
8. The method of claim 1 , wherein, for each of the one or more identified notifications, determining whether the calculated click-through probability satisfies the threshold comprises:
determining whether the calculated click-through probability exceeds a particular probability or is within a particular range of probabilities.
9. The method of claim 1 , wherein, for each of the one or more identified notifications, determining whether the calculated click-through probability satisfies a threshold comprises:
determining whether the triggering action took place within a timeframe within which the identified notification is valid.
10. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:
receive an indication that a triggering action has been detected from a client device;
identify one or more notifications associated with the triggering action, wherein the one or more notifications have been stored in a queue prior to receiving the indication that the triggering action has been detected; and
for each of the one or more identified notifications:
calculate a click-through probability that a user associated with the client device will interact with the notification, wherein the click-through probability is calculated based at least in part on a period the notification was stored in the queue prior to receiving the indication that the of the triggering action has been detected;
determine whether the calculated click-through probability satisfies a threshold; and
send, in response to determining that the calculated click-through probability satisfies the threshold, the identified notification to the client device.
11. The one or more computer-readable non-transitory storage media of claim 10 , wherein the triggering action comprises:
a click-through of an icon;
a mouseover of an icon; or
a request to log in to an online-networking system.
12. The one or more computer-readable non-transitory storage media of claim 10 , wherein, for each of the one or more identified notifications, the click-through probability is calculated further based on a machine-learning model.
13. The one or more computer-readable non-transitory storage media of claim 12 , further being operable when executed to:
detect one or more actions taken by the user upon display of one or more of the identified notifications; and
modify the machine-learning model based on the detected actions.
14. The one or more computer-readable non-transitory storage media of claim 10 , wherein one or more of the identified notifications are associated with an application, and wherein the click-through probability for each notification associated with the application is calculated further based on:
the user's frequency of interacting with the application;
the user's click-through rate for one or more notifications associated with the application that were previously sent to the client device;
a time of the user's most recent interaction with the application; or
a category corresponding to the application.
15. The one or more computer-readable non-transitory storage media of claim 10 , wherein one or more of the identified notifications are associated with an application, and wherein the click-through probability for each notification associated with the application is calculated further based on:
a number of daily active users of the application;
a number of monthly active users of the application;
a growth rate of the application's number of daily active users;
a growth rate of the application's number of monthly active users;
a conversion rate associated with users of the application fitting a particular demographic description; or
a language supported by the application.
16. A system comprising: one or more processors; and a memory coupled to the processors comprising instructions executable by the processors, the processors being operable when executing the instructions to:
receive an indication that a triggering action has been detected from a client device;
identify one or more notifications associated with the triggering action, wherein the one or more notifications have been stored in a queue prior to receiving the indication that the triggering action has been detected; and
for each of the one or more identified notifications:
calculate a click-through probability that a user associated with the client device will interact with the notification, wherein the click-through probability is calculated based at least in part on a period the notification was stored in the queue prior to receiving the indication that the of the triggering action has been detected;
determine whether the calculated click-through probability satisfies a threshold; and
send, in response to determining that the calculated click-through probability satisfies the threshold, the identified notification to the client device.
17. The system of claim 16 , wherein the triggering action comprises:
a click-through of an icon;
a mouseover of an icon; or
a request to log in to an online-networking system.
18. The system of claim 16 , wherein, for each of the one or more identified notifications, the click-through probability is calculated further based on a machine-learning model.
19. The system of claim 18 , further being operable when executing the instructions to:
detect one or more actions taken by the user upon display of one or more of the identified notifications; and
modify the machine-learning model based on the detected actions.
20. The system of claim 16 , wherein one or more of the identified notifications are associated with an application, and wherein the click-through probability for each notification associated with the application is calculated further based on:
the user's frequency of interacting with the application;
the user's click-through rate for one or more notifications associated with the application that were previously sent to the client device;
a time of the user's most recent interaction with the application; or
a category corresponding to the application.