This disclosure relates to systems and methods that include configuring a machine learning system to train on a plurality of messages, the machine learning system to output an expected number of positive responses and an expected number of negative responses based on an input message, determining a threshold differential and a weight value using responses to the plurality of messages, and sending the input message in response to a differential between the expected number of positive responses and the weight multiplied by the expected number of negative responses being above the threshold differential.
Please help me write a proper abstract based on the patent claims. CLAIM: 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 to output an expected number of positive responses and an expected number of negative responses based on an input message; determine a threshold differential and a weight value using responses to the plurality of messages to minimize a number of messages to send while satisfying one or more constraints, the constraints comprising a maximum number of negative responses and a minimum number of positive responses; and send the input message in response to a differential between the expected number of positive responses and the weight multiplied by the expected number of negative responses being above the threshold differential. 2. The system of claim 1, wherein the instructions further cause the system to select a channel for the input message that results in a highest difference between the expected number of positive responses and the expected number of negative responses multiplied by the weight, the system sending the input message via the selected channel in response to the highest difference exceeding the threshold differential. 3. The system of claim 2, wherein the channel is selected from the group consisting of email, mobile text messaging, an application notification, and a profile message. 4. The system of claim 1, wherein the instructions further cause the system to select a time of day to send the input message that results in a highest difference between the expected number of positive responses and the expected number of negative responses multiplied by the weight, the system sending the input message at the time of day in response to the highest difference exceeding the threshold differential. 5. The system of claim 1, wherein the expected number of positive responses is based on at least one of page views, clicked links, a purchases, likes, and comments, and the expected number of negative responses are based on at least one of unsubscribes, complaints, dislikes, and spam reports. 6. The system of claim 1, wherein the machine learning system is configured to train on a user's activity for a threshold period of time based on a response to one of the plurality of messages. 7. The system of claim 8, wherein the user's activity is tracked using the user's network address. 8. The system of claim 1, wherein determining the threshold differential and the weight is based on a multi-objective optimization problem using the constraints. 9. The system of claim 1, wherein determining the threshold differential and the weight is accomplished by applying a set of estimated threshold differentials and estimated weights and selecting the threshold differential and the weight from the set that minimizes the number of messages to send while satisfying the constraints. 10. A method comprising: configuring a machine learning system to train on a plurality of messages, the machine learning system to output an expected number of positive responses and an expected number of negative responses based on an input message; determining a threshold differential and a weight value using responses to the plurality of messages to minimize a number of message to send and satisfy one or more constraints, the constraints comprising a maximum number of negative responses, and a minimum number of positive responses; and sending the input message in response to a differential between the expected number of positive responses and the weight multiplied by the expected number of negative responses being above the threshold differential. 11. The method of claim 10, further comprising selecting a channel for the input message that results in a highest difference between the expected number of positive responses and the expected number of negative responses multiplied by the weight, the system sending the input message via the selected channel in response to the highest difference exceeding the threshold differential. 12. The method of claim 11, wherein the channel is selected from the group consisting of email, mobile text messaging, an application notification, and a profile message. 13. The method of claim 10, further comprising selecting a time of day to send the input message that results in a highest difference between the expected number of positive responses and the expected number of negative responses multiplied by the weight, the system sending the input message at the time of day in response to the highest difference exceeding the threshold differential. 14. The method of claim 10, wherein the expected number of positive responses is based on at least one of page views, clicked links, a purchases, likes, and comments, and the expected number of negative responses are based on at least one of unsubscribes, complaints, dislikes, and spam reports. 15. The method of claim 10, wherein the machine learning system is configured to train on a user's activity for a threshold period of time based on a response to one of the plurality of messages. 16. The method of claim 15, wherein the user's activity is tracked using the user's network address. 17. The method of claim 10, wherein determining the threshold differential and the weight is based on a multi-objective optimization problem using the constraints. 18. The method of claim 10, wherein determining the threshold differential and the weight is accomplished by applying a set of estimated threshold differentials and estimated weights and selecting the threshold differential and the weight from the set that minimize the number of messages to send while satisfying the constraints. 19. 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 to output an expected number of positive responses and an expected number of negative responses based on an input message; determine a threshold differential and a weight value using responses to the plurality of messages to minimize a number of message to send and satisfy one or more constraints, the constraints comprising a maximum number of negative responses, and a minimum number of positive responses; and send the input message in response to a differential between the expected number of positive responses and the weight multiplied by the expected number of negative responses being above the threshold differential. 20. The machine-readable medium of claim 19, wherein the instructions further comprise selecting a channel for the input message that results in a highest difference between the expected number of positive responses and the expected number of negative responses multiplied by the weight, the system sending the input message via the selected channel in response to the highest difference exceeding the threshold differential.