Methods and systems for generating a multiple user profile
The present disclosure is directed to methods and systems generating a multiple user profile. The profile system can create a multiple user profile based on metadata associate with two or more user profiles. The multiple user profile can include media content for the users to consume together based on shared attributes between the individual profiles of the users. The profile system can determine profiles that have similar attributes and send a recommendation for the users to join a group profile.
1 . A method comprising:
receiving, from a first user device associated with a first user, a request for a recommendation of a user to consume content together with the first user;
determining a first set of attributes of media content consumed by the first user associated with a first user profile;
determining a second set of attributes of media content consumed by a second user associated with a second user profile;
assigning a first score to a type of media content consumed by the first user based on the first set of attributes;
assigning a second score to the type of media content consumed by the second user based on the second set of attributes;
in response to the first score being within a threshold difference of the second score,
generating a multiple user profile for the first user and the second user to access media content together, wherein the multiple user profile includes the type of media content,
identify at least one media content item that has at least one common attribute among the first user profile and the second user profile, and
sending a notification to the first user device to recommend the first user consume content together with the second user,
wherein the notification includes the at least one media content item.
2 . The method of claim 1 , further comprising:
generating, based on at least one attribute of the multiple user profile, a recommendation of media content for the first user profile or the second user profile; and
sending the recommendation to the first user profile or the second user profile.
3 . The method of claim 1 , further comprising:
determining a third set of attributes of media content consumed by a third user associated with a third user profile;
assigning a third score to the type of media content consumed by the third user based on the third set of attributes;
in response to the first score being with the threshold difference of the third score, generating a recommendation for the first user to consume content with the third user; and
sending, to the first user and the third user, the recommendation to consume content together.
4 . The method of claim 1 , further comprising:
sending, from the first user profile to the second user profile, a recommendation for the second user profile to consume a media content item.
5 . The method of claim 1 , further comprising:
receiving a request for a content item recommendation for three or more users to consume together;
analyzing three or more user profiles associated with the three or more users to identify at least one media content item that has at least one common attributed among the three or more user profiles; and
sending, to at least one profile of the three or more user profiles, a recommendation of the at least one media content item.
6 . The method of claim 1 , wherein the first score is based on a duration the first user consumes the type of media content, a frequency that the first user consumes the type of media content, or feedback from the first user regarding the type of media content.
7 . The method of claim 1 , wherein the multiple user profile is generated by at least one machine-learning algorithm, wherein the at least one machine-learning algorithm is trained based on at least one dataset associated with previously generated multiple user profiles.
8 . A non-transitory computer-readable medium storing instructions that, when executed by a computing system, cause the computing system to perform operations comprising:
receiving, from a first user device associated with a first user, a request for a recommendation of a user to consume content together with the first user;
determining a first set of attributes of media content consumed by the first user associated with a first user profile;
determining a second set of attributes of media content consumed by a second user associated with a second user profile;
assigning a first score to a type of media content consumed by the first user based on the first set of attributes;
assigning a second score to the type of media content consumed by the second user based on the second set of attributes;
in response to the first score being within a threshold difference of the second score,
generating a multiple user profile for the first user and the second user to access media content together, wherein the multiple user profile includes the type of media content,
identify at least one media content item that has at least one common attribute among the first user profile and the second user profile, and
sending a notification to the first user device to recommend the first user consume content together with the second user,
wherein the notification includes the at least one media content item.
9 . The non-transitory computer-readable medium of claim 8 , wherein the operations further comprise:
generating, based on at least one attribute of the multiple user profile, a recommendation of media content for the first user profile or the second user profile; and
sending the recommendation to the first user profile or the second user profile.
10 . The non-transitory computer-readable medium of claim 8 , wherein the operations further comprise:
determining a third set of attributes of media content consumed by a third user associated with a third user profile;
assigning a third score to the type of media content consumed by the third user based on the third set of attributes;
in response to the first score being with the threshold difference of the third score, generating a recommendation for the first user to consume content with the third user; and
sending, to the first user and the third user, the recommendation to consume content together.
11 . The non-transitory computer-readable medium of claim 8 , wherein the operations further comprise:
sending, from the first user profile to the second user profile, a recommendation for the second user profile to consume a media content item.
12 . The non-transitory computer-readable medium of claim 8 , wherein the operations further comprise:
receiving a request for a content item recommendation for three or more users to consume together;
analyzing three or more user profiles associated with the three or more users to identify at least one media content item that has at least one common attributed among the three or more user profiles; and
sending, to at least one profile of the three or more user profiles, a recommendation of the at least one media content item.
13 . The non-transitory computer-readable medium of claim 8 , wherein the first score is based on a duration the first user consumes the type of media content, a frequency that the first user consumes the type of media content, or feedback from the first user regarding the type of media content.
14 . The non-transitory computer-readable medium of claim 8 , wherein the multiple user profile is generated by at least one machine-learning algorithm, wherein the at least one machine-learning algorithm is trained based on at least one dataset associated with previously generated multiple user profiles.
15 . A system comprising:
one or more processors; and
one or more memories storing instructions that, when executed by the one or more processors, cause the system to perform a process comprising:
receiving, from a first user device associated with a first user, a request for a recommendation of a user to consume content together with the first user;
determining a first set of attributes of media content consumed by the first user associated with a first user profile;
determining a second set of attributes of media content consumed by a second user associated with a second user profile;
assigning a first score to a type of media content consumed by the first user based on the first set of attributes;
assigning a second score to the type of media content consumed by the second user based on the second set of attributes;
in response to the first score being within a threshold difference of the second score,
generating a multiple user profile for the first user and the second user to access media content together, wherein the multiple user profile includes the type of media content,
identify at least one media content item that has at least one common attribute among the first user profile and the second user profile, and
sending a notification to the first user device to recommend the first user consume content together with the second user, wherein the notification includes the at least one media content item.
16 . The system according to claim 15 , wherein the process further comprises:
generating, based on at least one attribute of the multiple user profile, a recommendation of media content for the first user profile or the second user profile; and
sending the recommendation to the first user profile or the second user profile.
17 . The system according to claim 15 , wherein the process further comprises:
determining a third set of attributes of media content consumed by a third user associated with a third user profile;
assigning a third score to the type of media content consumed by the third user based on the third set of attributes;
in response to the first score being with the threshold difference of the third score, generating a recommendation for the first user to consume content with the third user; and
sending, to the first user and the third user, the recommendation to consume content together.
18 . The system according to claim 15 , wherein the process further comprises:
sending, from the first user profile to the second user profile, a recommendation for the second user profile to consume a media content item.
19 . The system according to claim 15 , wherein the process further comprises:
receiving a request for a content item recommendation for three or more users to consume together;
analyzing three or more user profiles associated with the three or more users to identify at least one media content item that has at least one common attributed among the three or more user profiles; and
sending, to at least one profile of the three or more user profiles, a recommendation of the at least one media content item.
20 . The system according to claim 15 , wherein the multiple user profile is generated by at least one machine-learning algorithm, wherein the at least one machine-learning algorithm is trained based on at least one dataset associated with previously generated multiple user profiles.