Techniques for context sensitive illustrated graphical user interface elements
Techniques for context sensitive illustrated graphical user interface elements, comprising a social networking application. The social networking application may include, among other components, a social networking graphical elements management component to provide a list of recommended graphical element identifier information, in response to a request for the list of recommended graphical element identifier information, a context determination component to determine user context information representative of the at least one user context based at least partially on user communications intent information, user profile information, device location information, calendar event information, and/or external event information, and a graphical elements recommendation component to generate the list of recommended graphical element identifier information based at least partially on the user profile information, graphical elements promotion information, and/or the user context information.
1. A method comprising:
receiving, from a client device associated with a user, an indication to provide recommended stickers;
generating, by at least one computing device of a social networking system, a list of recommended stickers for the user based on context information associated with the user and based on machine learned context information corresponding to historical sticker use;
ranking the list of recommended stickers based on the context information associated with the user; and
providing one or more recommended stickers from the list of recommended stickers to the client device for display to the user based on the ranking.
2. The method as recited in claim 1 , wherein the context information comprises at least one of user communications intent information, user profile information, device location information, or event information.
3. The method as recited in claim 1 , wherein the machine learned context information indicates a machine learned correlation between one or more context terms and one or more stickers.
4. The method as recited in claim 1 , wherein the machine learned context information indicates a machine learned correlation between one or more users and one or more stickers.
5. The method as recited in claim 1 , wherein ranking the list of recommended stickers comprises ranking the list of recommended stickers based on a machine learned popularity specific to the context information associated with the user.
6. The method as recited in claim 5 , further comprising generating a popularity coefficient for each of the list of recommended stickers based on the context information associated with the user.
7. The method as recited in claim 1 , further comprising causing the client device to display the one or more recommended stickers within a messaging application.
8. A system comprising:
one or more memory devices storing context information associated with a user; and
one or more computing devices that cause the system to:
receive, from a client device associated with the user, an indication to provide recommended stickers;
generate a list of recommended stickers for the user based on the context information associated with the user and based on machine learned context information corresponding to historical sticker use;
rank the list of recommended stickers based on the context information associated with the user; and
provide one or more recommended stickers from the list of recommended stickers to the client device for display to the user based on the ranking.
9. The system as recited in claim 8 , wherein the context information comprises at least one of user communications intent information, user profile information, device location information, or event information.
10. The system as recited in claim 8 , wherein the machine learned context information indicates a machine learned correlation between one or more context terms and one or more stickers.
11. The system as recited in claim 8 , wherein the machine learned context information indicates a machine learned correlation between one or more users and one or more stickers.
12. The system as recited in claim 8 , wherein ranking the list of recommended stickers comprises ranking the list of recommended stickers based on a machine learned popularity specific to the context information associated with the user.
13. The system as recited in claim 12 , wherein the one or more computing devices further cause the system to generate a popularity coefficient for each of the list of recommended stickers based on the context information associated with the user.
14. The system as recited in claim 8 , wherein the one or more computing devices further cause the system to cause the client device to display the one or more recommended stickers within a messaging application.
15. A non-transitory computer-readable medium storing instructions thereon that, when executed by at least one processor, cause at least one computing device to:
receive, from a client device associated with a user, an indication to provide recommended stickers;
generate a list of recommended stickers for the user based on context information associated with the user and based on machine learned context information corresponding to historical sticker use;
rank the list of recommended stickers based on the context information associated with the user; and
provide one or more recommended stickers from the list of recommended stickers to the client device for display to the user based on the ranking.
16. The non-transitory computer-readable medium as recited in claim 15 , wherein the context information comprises at least one of user communications intent information, user profile information, device location information, or event information.
17. The non-transitory computer-readable medium as recited in claim 15 , wherein the machine learned context information indicates a machine learned correlation between one or more context terms and one or more stickers.
18. The non-transitory computer-readable medium as recited in claim 15 , wherein the machine learned context information indicates a machine learned correlation between one or more users and one or more stickers.
19. The non-transitory computer-readable medium as recited in claim 15 , wherein ranking the list of recommended stickers comprises ranking the list of recommended stickers based on a machine learned popularity specific to the context information associated with the user.
20. The non-transitory computer-readable medium as recited in claim 19 , wherein the instructions, when executed by the at least one processor, further cause the at least one computing device to generate a popularity coefficient for each of the list of recommended stickers based on the context information associated with the user.