Integrated profile creation for a social network environment
View Patent ↗In an example, a system and method is disclosed for providing a user matching system. To ease creation of a profile, a node may be enabled to connect to a third-party social media site where the node already has an established profile. The established profile is imported, wherein common fields are directly mapped, inferred fields may be deterministically inferred, and other fields such as “likes” may be assigned relative weights based on recentness, subjective user rankings, or correlation to other “likes.” The node may also be enabled to permanently log in to the matching system via credentials for the third-party social media site.
1. A method, comprising:
receiving credentials for a third-party service from a node;
authenticating the node with the third-party service using the credentials;
receiving profile data from the third-party service; and
creating a new user profile for the node based on the profile data from the third-party service;
parsing additional fields in the profile data from the third-party service; and
mapping the additional fields to the new user profile;
wherein mapping the additional fields to the new user profile comprises assigning relative weights to the additional fields.
2. The method of claim 1 further comprising mapping common fields from the profile data from the third-party service to the new user profile, and inferring fields for the new user profile from fields in the profile data from the third-party service.
3. The method of claim 1 , wherein authenticating the node comprises assigning the node a profile score, and inferring that the node is legitimate if the profile score is above a threshold.
4. The method of claim 1 , wherein assigning relative weights to the additional fields comprises correlating at least one additional field to at least one other additional field and inferring a weight from the correlation.
5. The method of claim 1 further comprising building an automatically-generated essay from at least one of the profile data from the third-party service and the new user profile.
6. The method of claim 1 , wherein assigning relative weights to the additional fields comprises correlating common data from a plurality of open-ended data fields.
7. The method of claim 1 , wherein assigning relative weights to the additional fields comprises aggregating a plurality of expressed preferences, and inferring a weight for a preference based at least in part on a common trait with another preference.
8. A non-transitory tangible computer-readable medium having stored thereon instructions operable to instruct a processor to:
receive credentials for a third-party service from a node;
authenticate the node with the third-party service using the credentials;
receive profile data from the third-party service; and
create a new user profile for the node based on the profile data from the third-party service
parse additional fields in the profile data from the third-party service; and
map the additional fields to the new user profile;
wherein mapping the additional fields to the new user profile comprises assigning relative weights to the additional fields.
9. The computer-readable medium of claim 8 , wherein the instructions are further operable to map common fields from the profile data from the third-party service to the new user profile, and infer fields for the new user profile from fields in the profile data from the third-party service.
10. The computer-readable medium of claim 8 , assigning the node a profile score, and inferring that the node is legitimate if the profile score is above a threshold.
11. The computer-readable medium of claim 8 , wherein assigning relative weights to the additional fields comprises correlating at least one additional field to at least one other additional field and inferring a weight from the correlation.
12. The computer-readable medium of claim 8 , wherein the instructions are further operable to build an automatically-generated essay from at least one of the profile data from the third-party service and the new user profile.
13. The computer-readable medium of claim 8 , wherein assigning relative weights to the additional fields comprises correlating common data from a plurality of open-ended data fields.
14. The computer-readable medium of claim 8 , wherein assigning relative weights to the additional fields comprises aggregating a plurality of expressed preferences, and inferring a weight for a preference based at least in part on a common trait with another preference.
15. A server comprising:
a processor, a memory, and a network connection, wherein the server is configured to:
receive credentials for a third-party service from a node;
authenticate the node with the third-party service using the credentials;
receive profile data from the third-party service;
create a new user profile for the node based on the profile data from the third-party service;
parse additional fields in the profile data from the third-party service; and
map the additional fields to the new user profile;
wherein mapping the additional fields to the new user profile comprises assigning relative weights to the additional fields.
16. The server of claim 15 , wherein the server is further configured to map common fields from the profile data from the third-party service to the new user profile, and infer fields for the new user profile from fields in the profile data from the third-party service.
17. The server of claim 15 , assigning the node a profile score, and inferring that the node is legitimate if the profile score is above a threshold.
18. The server of claim 15 , wherein assigning relative weights to the additional fields comprises correlating at least one additional field to at least one other additional field and inferring a weight from the correlation.
19. The server of claim 15 , wherein the server is further configured to build an automatically-generated essay from at least one of the profile data from the third-party service and the new user profile.
20. The server of claim 15 , wherein assigning relative weights to the additional fields comprises correlating common data from a plurality of open-ended data fields.
21. The server of claim 15 , wherein assigning relative weights to the additional fields comprises aggregating a plurality of expressed preferences, and inferring a weight for a preference based at least in part on a common trait with another preference.