Dynamically detecting user personas of network users for customized suggestions

1. A method comprising:

obtaining user activity of a plurality of users across a plurality of historical sessions;

determining a plurality of personas by clustering similar user activity across the plurality of historical sessions;

mining a plurality of sequential patterns in a first set of user activity data associated with a first persona of the plurality of personas; and

determining at least one predictive rule associated with the first persona, each predictive rule comprising an initial activity among the first set of user activity data and at least one subsequent activity in the first set of user activity data.

2. The method of claim 1 , further comprising:

obtaining a current set of user activity data from a current session of a user among the plurality of users;

dynamically determining a user objective for the user based on associating the current set of user activity data with the first persona; and

facilitating the user objective based on the at least one predictive rule associated with the first persona.

3. The method of claim 2 , wherein facilitating the user objective comprising adjusting a user interface of a user device to facilitate the at least one subsequent activity of the at least one predictive rule after detecting the initial activity.

4. The method of claim 2 , wherein dynamically determining the user objective for the user comprising associating the current set of user activity data with a second persona, wherein the second persona is associated with one or more different predictive rules.

5. The method of claim 1 , further comprising tracking which persona among the plurality of personas is associated with a particular user among the plurality of users for each historical session among the plurality of historical sessions that are associated with the particular user.

6. The method of claim 1 , wherein determining the plurality of personas by clustering similar user activity across the plurality of historical sessions comprises:

selecting a number k of personas in the plurality of personas based on a predetermined number of expected personas; and

clustering the user activity across the plurality of historical sessions with a k-means clustering algorithm.

7. The method of claim 1 , wherein the at least one subsequent activity in the first set of user activity data is within a predetermined time window of the initial activity.

8. An apparatus comprising:

a network interface configured to communicate with computing devices in a computer network; and

a processor coupled to the network interface, the processor configured to:

obtain via the network interface, user activity of a plurality of users across a plurality of historical sessions;

determine a plurality of personas by clustering similar user activity across the plurality of historical sessions;

mine a plurality of sequential patterns in a first set of user activity data associated with a first persona of the plurality of personas; and

determine at least one predictive rule associated with the first persona, each predictive rule comprising an initial activity among the first set of user activity data and at least one subsequent activity in the first set of user activity data.

9. The apparatus of claim 8 , wherein the processor is further configured to:

obtain via the network interface, a current set of user activity data from a current session of a user among the plurality of users;

dynamically determine a user objective for the user based on associating the current set of user activity data with the first persona; and

facilitate the user objective based on the at least one predictive rule associated with the first persona.

10. The apparatus of claim 9 , wherein the processor is configured to facilitate the user objective by adjusting a user interface of a user device via the network interface to facilitate the at least one subsequent activity of the at least one predictive rule after detecting the initial activity.

11. The apparatus of claim 9 , wherein the processor is configured to dynamically determine the user objective for the user by associating the current set of user activity data with a second persona, wherein the second persona is associated with one or more different predictive rules.

12. The apparatus of claim 8 , wherein the processor is further configured to track which persona among the plurality of personas is associated with a particular user among the plurality of users for each historical session among the plurality of historical sessions that are associated with the particular user.

13. The apparatus of claim 8 , wherein the processor is configured to determine the plurality of personas by:

selecting a number k of personas in the plurality of personas based on a predetermined number of expected personas; and

clustering the user activity across the plurality of historical sessions with a k-means clustering algorithm.

14. The apparatus of claim 8 , wherein the at least one subsequent activity in the first set of user activity data is within a predetermined time window of the initial activity.

15. One or more non-transitory computer readable storage media encoded with software comprising computer executable instructions that, when the software is executed on a computing device, is operable to cause a processor of the computing device to:

obtain user activity of a plurality of users across a plurality of historical sessions;

determine a plurality of personas by clustering similar user activity across the plurality of historical sessions;

mine a plurality of sequential patterns in a first set of user activity data associated with a first persona of the plurality of personas; and

determine at least one predictive rule associated with the first persona, each predictive rule comprising an initial activity among the first set of user activity data and at least one subsequent activity in the first set of user activity data.

16. The one or more non-transitory computer readable storage media of claim 15 , wherein the software is further operable to cause the processor to:

obtain a current set of user activity data from a current session of a user among the plurality of users;

dynamically determine a user objective for the user based on associating the current set of user activity data with the first persona; and

facilitate the user objective based on the at least one predictive rule associated with the first persona.

17. The one or more non-transitory computer readable storage media of claim 16 , wherein the software is further operable to cause the processor to facilitate the user objective by adjusting a user interface of a user device to facilitate the at least one subsequent activity of the at least one predictive rule after detecting the initial activity.

18. The one or more non-transitory computer readable storage media of claim 16 , wherein the software is further operable to cause the processor to dynamically determine the user objective for the user by associating the current set of user activity data with a second persona, wherein the second persona is associated with one or more different predictive rules.

19. The one or more non-transitory computer readable storage media of claim 15 , wherein the software is further operable to cause the processor to track which persona among the plurality of personas is associated with a particular user among the plurality of users for each historical session among the plurality of historical sessions that are associated with the particular user.

20. The one or more non-transitory computer readable storage media of claim 15 , wherein the software is further operable to cause the processor to determine the plurality of personas by:

selecting a number k of personas in the plurality of personas based on a predetermined number of expected personas; and

clustering the user activity across the plurality of historical sessions with a k-means clustering algorithm.