User to user recommender
First Claim
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 one or more of the users in the user set to assess similarity of the target user profile to each of the other one or more users'"'"' profiles as a proxy for compatibility of the corresponding users;
classifying a relationship type between the target user and each of the one or more of the users in the user set as one of a predetermined number of relevant relationship types; and
generating a recommended set of compatible users for the target user responsive to the comparing and classifying steps, wherein the recommended set of compatible users comprises at least one user within the selected user set; and
furtherwherein 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 at least the corresponding user'"'"'s media item set or recommended 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.
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Accused Products
Abstract
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.
268 Citations
27 Claims
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1. A method for recommending users in a user community to a target user, the method comprising:
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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 one or more of the users in the user set to assess similarity of the target user profile to each of the other one or more users'"'"' profiles as a proxy for compatibility of the corresponding users; classifying a relationship type between the target user and each of the one or more of the users in the user set as one of a predetermined number of relevant relationship types; and generating a recommended set of compatible users for the target user responsive to the comparing and classifying steps, wherein the recommended set of compatible users comprises at least one user within the selected user set; and
furtherwherein 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 at least the corresponding user'"'"'s media item set or recommended 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. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A non-transitory computer-readable medium having stored thereon computer executable instructions for performing a method for recommending users in a user community, the method comprising:
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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 one or more of the users in the user set; classifying a relationship type between the first user and each of the one or more of the users in the user set as one of a predetermined number of relevant relationship types; and generating a recommended user set for the first user responsive to the comparing and classifying steps, wherein the recommended user set comprises at least one user within the user community; and
furtherwherein 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 at least the corresponding user'"'"'s media item set or recommended 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. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15)
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16. A non-transitory computer-readable medium having stored thereon computer executable instructions for performing a method for recommending users in a user community, the method comprising:
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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 one or more of the users in the user set; classifying a relationship type between the first user and each of the one or more of the users in the user set as one of a predetermined number of relevant relationship types; and generating a recommended user set for the first user responsive to the comparing and classifying steps, wherein the recommended user set comprises at least one user within the user community; and
furtherwherein the selected first user'"'"'s profile comprises a first user media item set and a first user recommended media item set; and the user profiles for each of the users in the selected user set comprises a corresponding user media item set or a corresponding user recommended media item set; and each recommended media item set is generated by providing the corresponding user'"'"'s media item set or recommended media item set as input to a computer-implemented recommender system, wherein the computer-implemented recommender system generates the recommended media item set based on a predetermined knowledge base stored in memory that contains concurrency metrics among collections of mediasets, the concurrency metrics associating each media item in the user'"'"'s media item set with at least one media item in the recommended media item set. - View Dependent Claims (17)
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18. A method for recommending users in a user community to a target user, the method comprising:
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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 one or more of the users in the user set to assess similarity of the target user profile to each of the one or more users'"'"' profiles as a proxy for compatibility of the corresponding users; classifying a relationship type between the target user and each of the one or more of the users in the user set as one of a predetermined number of relevant relationship types; and generating a recommended set of compatible users for the target user responsive to the comparing and classifying steps, wherein the recommended set of compatible users comprises at least one user within the selected user set; and
furtherwherein 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 or a corresponding user recommended media item set; and each recommended media item set is generated by providing at least the corresponding user'"'"'s media item set or recommended media item set as input to a computer-implemented recommender system, wherein the computer-implemented recommender system generates the recommended media item set based on a predetermined knowledge base stored in memory that contains concurrency metrics among collections of mediasets, the concurrency metrics associating each media item in the user'"'"'s media item set with at least one media item in the recommended media item set. - View Dependent Claims (19, 20, 21, 22, 23, 24, 25, 26, 27)
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Specification