Content generation and targeting using machine learning
A system and method for content generation and targeting using machine learning are provided. In example embodiments, a probability that a user will visit a webpage based on historical data is calculated. A probability that the user will engage with a particular content category based on past user engagement is calculated. In response to the probability of the user engaging with the particular content category being equal to or greater than a first threshold, the content is generated. Further, in response to the probability of the user not visiting a webpage meeting or exceeding a second threshold, the generated content is sent to the user.
1. A system comprising:
one or more hardware processors, and a computer-readable memory including instructions, which when executed by the one or more hardware processors, configure the system to perform a plurality of operations comprising:
calculating a probability that a user will not visit a webpage with a predetermined period of time, based on historical data about past visits to the webpage;
calculating a probability that the user will engage with a particular content category based on past user engagement;
in response to the probability of the user engaging with the particular content category being equal to or greater than a first threshold, generating content from the particular content category;
in response to the probability of the user not visiting a webpage meeting or exceeding a second threshold, sending the generated content to the user.
2. The system of claim 1 , wherein:
the calculating of the probability that the user will visit a webpage is further based on user profile information associated with the user.
3. The system of claim 1 , further comprising:
identifying other members of a social network service based on a comparison between the user profile and other members profiles associated with the other members; and
wherein the historical data includes historical data of the user and other members identified to be similar to the user.
4. The system of claim 1 , wherein:
the historical data include activities that indicate the user engaged with a social network service.
5. The system of claim 1 , wherein:
the calculating of the probability that the user will engage with a particular content category is further based on information specific to the user.
6. The system of claim 1 , wherein:
the calculating of the probability that the user will engage with a particular content category is further based on a content feature that elicited past user action.
7. The system of claim 1 , wherein:
the calculating of the probability that the user will visit a webpage is based on machine learning using a logistic regression model.
8. The system of claim 1 wherein:
the probability of the user visiting the webpage is further based on the user visiting the webpage within a predetermined time period.
9. The system of claim 1 , wherein:
the user engaging with the content includes the user clicking on the content and is redirected to a corresponding webpage.
10. The system of claim 1 , further comprising:
determining a communication channel to interact with the user based on past user engagement with the communication channel selected from a plurality of communication channels; and
wherein, the sending the generated content is sent via the determined communication channel.
11. A method comprising:
using one or more computer processors:
calculating a probability that a user will not visit a webpage with a predetermined period of time, based on historical data about past visits to the webpage;
calculating a probability that the user will engage with a particular content category based on past user engagement;
in response to the probability of the user engaging with the particular content category being equal to or greater than a first threshold, generating content from the particular content category; and
in response to the probability of the user not visiting a webpage meeting or exceeding a second threshold, sending the generated content to the user.
12. The method of claim 11 , wherein:
the historical data includes a user profile for the user and member profiles of other members; and
identifying similar members that are similar to the user by comparing the user profile with respective member profiles of the other members.
13. The method of claim 11 , wherein:
the calculating of the probability that the user will engage with a particular content category is further based on information specific to the user.
14. The method of claim 11 , wherein:
the calculating of the probability that the user will engage with a particular content category is further based on a content feature that elicited past user action.
15. The method of claim 11 , wherein:
the calculating of the probability that the user will visit a webpage is based on machine learning using a logistic regression model.
16. The method of claim 11 , wherein:
the probability of the user visiting the webpage is further based on the user visiting the webpage within a predetermined time period.
17. The method of claim 11 , wherein:
the user engaging with the content includes the user clicking on the content and is redirected to a corresponding webpage.
18. The method of claim 11 , further comprising:
determining a communication channel to interact with the user based on past user engagement with the communication channel selected from a plurality of communication channels; and
wherein, the sending the generated content is sent via the determined communication channel.
19. A machine-readable medium not having any transitory signals and storing instructions that, when executed by at least one processor of a machine, cause the machine to perform operations comprising:
calculating a probability that a user will not visit a webpage based on historical data;
calculating a probability that a user will not visit a webpage with a predetermined period of time, based on historical data about past visits to the webpage;
in response to the probability of the user engaging with the particular content category being equal to or greater than a first threshold, generating content from the particular content category;
in response to the probability of the user not visiting a webpage meeting or exceeding a second threshold, sending the generated content to the user.
20. The machine-readable medium of claim 19 , wherein the operations further comprise:
determining a communication channel to interact with the user based on past user engagement with the communication channel selected from a plurality of communication channels; and
wherein, the sending the generated content is sent via the determined communication channel.