Notification management and channel selection

A system comprising:

• a memory comprising instructions;
• and
• one or more computer processors, wherein the instructions, when executed by the one or more computer processors, cause the system to perform operations comprising:
• training respective neural networks to obtain a first model, a second model, and a third model, the first model generating an embedding for a member of an online service based on member information, the second model generating a first parameter for a distribution function and the third model generating a second parameter for the distribution function;
• accessing a notification for a first member;
• calculating, by the first model, a first member embedding;
• calculating, by the second model, a first parameter value based on the first member embedding;
• calculating, by the third model, a second parameter value based on the first member embedding;
• determining, based on the first parameter value and the second parameter value for the distribution, a first probability that the first member will visit the online service in response to the notification and a second probability that the first member will visit the online service without sending the notification;
• and
• determining to send the notification based on the first probability and the second probability.

A tangible machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations comprising:

• training respective neural networks to obtain a first model, a second model, and a third model, the first model generating an embedding for a member of an online service based on member information, the second model generating a first parameter for a distribution function and the third model generating a second parameter for the distribution function;
• accessing a notification for a first member;
• calculating, by the first model, a first member embedding;
• calculating, by the second model, a first parameter value based on the first member embedding;
• calculating, by the third model, a second parameter value based on the first member embedding;
• determining, based on the first parameter value and the second parameter value for the distribution, a first probability that the first member will visit the online service in response to the notification and a second probability that the first member will visit the online service without sending the notification;
• and
• determining to send the notification based on the first probability and the second probability.