DISTRIBUTION OF ELECTRONIC MESSAGES
This disclosure relates to systems and methods that include configuring a machine learning system to train on a plurality of messages, solving, for a set of input messages, a multi-objective optimization problem to minimize a number of messages to send while satisfying one or more constraints, selecting a random value for one or more message and message recipient pairs in the set of input messages, setting a send constraint for one or more of the pairs using a send threshold for the message in the set and the random value, and sending the message to a recipient for the message in the set in response to the send constraint for the pair being satisfied.
1 . A system comprising:
a machine-readable medium having instructions stored thereon, which, when executed by a processor, cause the system to:
configure a machine learning system to train on a plurality of messages, the machine learning system outputting an expected number of responses selected from a first set of responses to an input message and an expected number of responses selected from a second set of responses to the input message, the first set of responses being different than the second set of responses;
solve, for a set of input messages, a multi-objective optimization problem to minimize a number of messages to send while satisfying one or more constraints, the multi-objective optimization problem including the expected number of responses selected from the first set and the expected number of responses selected from the second set;
select a random value for one or more message and message recipient pairs in the set of input messages;
set a send constraint for one or more of the pairs using a send threshold for the message in the set and the random value; and
send the message to a recipient for the message in the set in response to the send constraint for the pair being satisfied.
2 . The system of claim 1 , wherein the one or more constraints includes the summation of send probabilities for each of the messages in the set of input messages being below a threshold value.
3 . The system of claim 1 , wherein the instructions further cause the system to remove messages from the input set of messages that are of type that a recipient member has requested to not receive.
4 . The system of claim 1 , wherein the set of input messages are divided according to a message type and one or more of the constraints includes a number of responses from messages that are of a specific message type being below a threshold value.
5 . The system of claim 4 , wherein the threshold value is a multiplier multiplied by a maximum number of responses from one of the sets of responses.
6 . The system of claim 5 , wherein the multiplier is either generated from the solution of the multi-objective optimization problem or received from an administrator of the system.
7 . The system of claim 1 , wherein solving the multi-objective optimization problem comprises solving the multi-objective optimization problem for two or more different message types.
8 . The system of claim 1 , wherein the machine learning system trains on responses that are downstream of messages from the system.
9 . The system of claim 1 , wherein messages in the set of input messages that are subscription messages are not included in the minimum number of messages to send.
10 . A method comprising:
configuring a machine learning system to train on a plurality of messages, the machine learning system outputting an expected number of responses selected from a first set of responses to an input message and an expected number of responses selected from a second set of responses to the input message, the first set of responses being different than the second set of responses;
solving, for a set of input messages, a multi-objective optimization problem to minimize a number of messages to send while satisfying one or more constraints, the multi-objective optimization problem including the expected number of responses selected from the first set and the expected number of responses selected from the second set;
selecting a random value for one or more message and message recipient pairs in the set of input messages;
setting a send constraint for one or more of the pairs using a send threshold for the message in the set and the random value; and
sending the message to a recipient for the message in the set in response to the send constraint for the pair being satisfied.
11 . The method of claim 10 , wherein the one or more constraints includes the summation of send probabilities for each of the messages in the set of input messages being below a threshold value.
12 . The method of claim 10 , wherein the one or more constraints includes the expected number of responses from one of the sets of responses being below a threshold number.
13 . The method of claim 10 , wherein the set of input messages are divided according to a message type and one or more of the constraints includes a number of responses from messages that are of a specific message type being below a threshold value.
14 . The method of claim 13 , wherein the threshold value is a multiplier multiplied by a maximum number of responses from one of the sets of responses.
15 . The method of claim 14 , wherein the multiplier is either generated from the solution of the multi-objective optimization problem or received from an administrator of the system.
16 . The method of claim 10 , wherein solving the multi-objective optimization problem comprises solving the multi-objective optimization problem for two or more different message types.
17 . The method of claim 10 , wherein the machine learning system trains on responses that are downstream of messages from the system.
18 . A non-transitory machine-readable medium having instructions stored thereon, which, when executed by a hardware processor, cause the system to:
configure a machine learning system to train on a plurality of messages, the machine learning system outputting an expected number of responses selected from a first set of responses to an input message and an expected number of responses selected from a second set of responses to the input message, the first set of responses being different than the second set of responses;
solve, for a set of input messages, a multi-objective optimization problem to minimize a number of messages to send while satisfying one or more constraints, the multi-objective optimization problem including the expected number of responses selected from the first set and the expected number of responses selected from the second set;
select a random value for one or more message and message recipient pairs in the set of input messages;
set a send constraint for one or more of the pairs using a send threshold for the message in the set and the random value; and
send the message to a recipient for the message in the set in response to the send constraint for the pair being satisfied.
19 . The system of claim 18 , wherein the set of input messages are divided according to a message type and one or more of the constraints includes a number of responses from messages that are of a specific message type being below a threshold value.
20 . The system of claim 18 , wherein the machine learning system trains on responses that are downstream of messages from the system.