User to user recommender
Disclosed are embodiments of systems and methods for recommending relevant users to other users in a user community. In one implementation of such a method, two different sets of data are considered: a) music (or other items) that users have been listening to (or otherwise engaging), and b) music (or other items) recommendations that users have been given. In some embodiments, pre-computation methods allow the system to efficiently compare item sets and recommended item sets among the users in the community. Such comparisons may also comprise metrics that the system can use to figure out which users should be recommended for a given target user.
1. A method for recommending users in a user community to a target user, the method comprising:
selecting a target user within the user community;
selecting a user set within the user community excluding the target user;
comparing a user profile for the target user with user profiles for each of the users in the user set to assess similarity of the target user profile to each of the other users' profiles as a proxy for compatibility of the corresponding users; and
generating a recommended set of compatible users for the target user responsive to the comparing step, wherein the recommended set of compatible users comprises at least one user within the selected user set; and further
wherein the target user's profile comprises a target user media item set [IS] and a target user recommended media item set [RS];
the user profiles for each of the users in the user set comprises a corresponding user media item set and a corresponding user recommended media item set; and
each recommended media item set is generated by providing the corresponding user's media item set as input to a computer-implemented recommender system,
wherein the computer-implemented recommender system that generates the recommended media item set is driven by a predetermined knowledge base stored in a memory that contains correlation metrics among a collection of media data items.
2. A method for recommending users according to claim 1 , wherein:
the correlation metrics stored in the knowledge base driving the computer-implemented recommender system to generate the recommended media item set of each user's profile reflect subjective judgments of a universe of other users outside of the said user community.
3. A method for recommending users according to claim 1 , wherein:
the stored correlation metrics are not based on objective characterizing attributes of the selected media items.
4. A method for recommending users according to claim 1 , wherein:
the correlation metrics stored in the knowledge base driving the computer-implemented recommender system to generate the recommended media item set of each user's profile reflect the numbers of co-occurrences of pairs of media items together within the personal playlists of a universe of other users outside of the said user community, so that the correlation metrics are a function of the outside users' subjective decisions to include a pair of selected media items together within a data set or playlist, without regard to characterizing attributes of the selected media items.
5. A method for recommending users according to claim 1 , wherein:
said comparing the user profiles includes determining an intersection of the target user media item set with the respective recommended media item sets of each of the users in the selected user set.
6. A method for recommending users according to claim 1 , wherein:
said comparing the user profiles includes determining an intersection of the target user recommended media item set with the respective recommended media item sets for each of the users in the selected user set.
7. A method for recommending users according to claim 1 , wherein:
said comparing the user profiles includes determining an intersection of the target user recommended media item set with the respective media item sets for each of the users in the selected user set.
8. A computer-readable medium having stored thereon computer executable instructions for performing a method for recommending users in a user community, the method comprising:
selecting a first user within the user community;
selecting a user set within the user community;
comparing a user profile for the first user with user profiles for each of the users in the user set; and
generating a recommended user set for the first user responsive to the comparing step, wherein the recommended user set comprises at least one user within the user community; and further
wherein the selected first user's profile comprises a first user media item set and a first user recommended media item set;
the user profiles for each of the users in the selected user set comprises a corresponding user media item set and a corresponding user recommended media item set;
each recommended media item set is generated by providing the corresponding user's media item set as input to a computer-implemented recommender system;
and the computer-implemented recommender system that generates the recommended media item set is driven by a predetermined knowledge base stored in a memory that contains correlation metrics among a collection of media data items.
9. The computer-readable medium of claim 8 , wherein the comparing a user profile for the first user with user profiles for each of the users in the user set includes computing a total relevance of the target user to each of the users in the user set, wherein the total relevance is a function of at least one of an affinity metric, a discovery metric and a guidance metric; wherein
the affinity metric determines an intersection M a∩ M b of the target user's media set with the other users' respective media sets;
the discovery metric determines an intersection R a∩ M b of the other users' respective media sets with the target user's recommended media set; and
the guidance metric determines an intersection M a∩ R b of the target user's media set with the other users' respective recommended media sets.
10. The computer-readable medium of claim 8 , wherein the correlation metrics stored in the knowledge base driving the computer-implemented recommender system to generate the recommended media item set of each user's profile reflect subjective judgments of a universe of other users outside of the said user community.
11. A method for recommending users according to claim 8 , wherein:
the stored correlation metrics are not based on objective characterizing attributes of the selected media items.
12. A method for recommending users according to claim 8 , wherein:
the correlation metrics stored in the knowledge base driving the computer-implemented recommender system to generate the recommended media item set of each user's profile reflect the numbers of co-occurrences of pairs of media items together within the personal playlists of a universe of other users outside of the said user community, so that the correlation metrics are a function of the outside users' subjective decisions to include a pair of selected media items together within a data set or playlist, without regard to characterizing attributes of the selected media items.
13. A method for recommending users according to claim 8 , wherein:
said comparing the user profiles includes determining an intersection of the target user media item set with the respective recommended media item sets of each of the users in the selected user set.
14. A method for recommending users according to claim 8 , wherein:
said comparing the user profiles includes determining an intersection of the target user recommended media item set with the respective recommended media item sets for each of the users in the selected user set.
15. A method for recommending users according to claim 8 , wherein:
said comparing the user profiles includes determining an intersection of the target user recommended media item set with the respective media item sets for each of the users in the selected user set.