RECOMMENDER SYSTEM FOR IDENTIFYING A NEW SET OF MEDIA ITEMS RESPONSIVE TO AN INPUT SET OF MEDIA ITEMS AND KNOWLEDGE BASE METRICS

• US 20130191318A1
• Filed: 11/12/2012
• Published: 07/25/2013
• Est. Priority Date: 02/03/2005
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Martin, Francisco J., Shur, Jim, Torrens, Marc
• Granted Patent: US 9,576,056 B2
• Time in Patent Office: Days
• US Class Current: 706/50
• CPC Class Codes: G06F 16/435 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: H04N 21/466 Learning process for intelligent management, e.g. learning user preferences for recommending movies services using the results of monitoring in broadcast systems H04H60/61
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/4825 using a list of items to be played back in a given order, e.g. playlists
• CPC Class Codes: H04N 7/163 by receiver means only

Abstract

Systems and methods are disclosed for identifying a new set of media items in response to an input set (or “query set”) of media items and knowledge base metrics. The system uses a knowledge base consisting of a collection of mediasets. Various metrics among media items are considered by analyzing how the media items are grouped to form the mediasets in the knowledge base. Such association or “similarity” metrics are preferably stored in a matrix form that allows the system to efficiently identify a new set of media items that complements the input set of media items.

Claims

1. A method comprising:

storing a plurality of media sets in a knowledge base of media sets; and

preprocessing the knowledge base to quantify an association between pairs of media items in the knowledge base, comprising;

for each item in a mediaset, identifying every other item in the same mediaset, so as to define all of the pairs of media items in that mediaset;

repeating said identifying step for every other mediaset in the knowledge base, thereby defining every pair of items that appears in any mediaset in the knowledge base; and

for each identified pair of media items, adjusting a concurrency metric value responsive to each additional occurrence of the same pair of items in the knowledge base.

2. The method of claim 1, wherein the concurrency metric value is a co-concurrency metric value, and adjusting comprises incrementing the co-concurrency metric value responsive to every additional occurrence of the same pair of items in the knowledge base.

3. The method of claim 1, wherein:

the media items are ordered within the mediasets in the knowledge base;

identifying comprises defining ordered pairs of items that appear in any mediaset in the knowledge base in the same order;

the concurrency metric value is an ordered concurrency metric value, and adjusting comprises incrementing the ordered concurrency metric value responsive to every additional occurrence of the same ordered pair of items in the knowledge base.

4. The method of claim 1, further comprising:

receiving, from a client device, at least one input media item;

identifying a plurality of ordered pairs containing the input media item; and

selecting one or more of the ordered pairs by evaluating the metric value.

5. The method of claim 4, further comprising:

sending a recommendation output set of media items to the client device, the output set comprising media items paired with the input media item.

6. The method of claim 5, further comprising:

receiving, along with the input media item, one or more recommendation preferences from the client device relating to what types of media items a user prefers to receive in the recommendation output set of media items; and

filtering the output set according to the one or more recommendation preferences.

7. The method of claim 5, further comprising:

filtering the output set based on metadata of the input media item.

8. The method of claim 5, further comprising:

storing media library collection data for a plurality of user accounts in relation to the knowledge base;

upon receiving the input media item, accessing the user account associated with the client device; and

filtering the output set based on the user of the client device'"'"'s media library.

9. A system for preprocessing a knowledge base for sending media recommendations comprising:

a database for storing a plurality of media sets in a knowledge base of media sets; and

a processor for preprocessing the knowledge base to quantify an association between pairs of media items in the knowledge base, wherein the processor is further configured to;

for each item in a mediaset, identify every other item in the same mediaset, so as to define all of the pairs of media items in that mediaset;

repeat said identifying step for every other mediaset in the knowledge base, thereby defining every pair of items that appears in any mediaset in the knowledge base; and

for each identified pair of media items, adjust a concurrency metric value responsive to each additional occurrence of the same pair of items in the knowledge base.

10. The system of claim 9, wherein the concurrency metric value is a co-concurrency metric value, and wherein the processor is further configured increment the co-concurrency metric value responsive to every additional occurrence of the same pair of items in the knowledge base.

11. The system of claim 9, wherein the media items are ordered within the mediasets in the knowledge base, wherein the processor is configured to define ordered pairs of items that appear in any mediaset in the knowledge base in the same order, wherein the concurrency metric value is an ordered concurrency metric value, and wherein the processor is further configured to increment the ordered concurrency metric value responsive to every additional occurrence of the same ordered pair of items in the knowledge base.

12. The system of claim 9, wherein the processor is further configured to:

receive, from a client device, at least one input media item;

identify a plurality of ordered pairs containing the input media item; and

select one or more of the ordered pairs by evaluating the metric value.

13. The system of claim 12, wherein the processor is further configured to:

send a recommendation output set of media items to the client device, the output set comprising media items paired with the input media item.

14. The system of claim 13, wherein the processor is further configured to:

receive, along with the input media item, one or more recommendation preferences from the client device relating to what types of media items a user prefers to receive in the recommendation output set of media items; and

filter the output set according to the one or more recommendation preferences.

15. The system of claim 13, wherein the processor is further configured to:

filter the output set based on metadata of the input media item.

16. The system of claim 13, wherein the processor is further configured to:

store media library collection data for a plurality of user accounts in relation to the knowledge base;

upon receiving the input media item, access the user account associated with the client device; and

filter the output set based on the user of the client device'"'"'s media library.

21. The non-transitory computer-readable storage medium of claim 16, the instructions further comprising:

sending a recommendation output set of media items to the client device, the output set comprising media items paired with the input media item.

22. The non-transitory computer-readable storage medium of claim 21, the instructions further comprising:

receiving, along with the input media item, one or more recommendation preferences from the client device relating to what types of media items a user prefers to receive in the recommendation output set of media items; and

filtering the output set according to the one or more recommendation preferences.

23. The non-transitory computer-readable storage medium of claim 21, the instructions further comprising:

filtering the output set based on metadata of the input media item.

24. The non-transitory computer-readable storage medium of claim 21, the instructions further comprising:

storing media library collection data for a plurality of user accounts in relation to the knowledge base;

upon receiving the input media item, accessing the user account associated with the client device; and

filtering the output set based on the user of the client device'"'"'s media library.

17. A non-transitory computer-readable medium comprising:

a medium configured to store computer-readable instructions thereon; and

the computer-readable instructions that, when executed by a processing device cause the processing device to perform a method, comprising;

storing a plurality of media sets in a knowledge base of media sets; and

preprocessing the knowledge base to quantify an association between pairs of media items in the knowledge base, comprising;

for each item in a mediaset, identifying every other item in the same mediaset, so as to define all of the pairs of media items in that mediaset;

repeating said identifying step for every other mediaset in the knowledge base, thereby defining every pair of items that appears in any mediaset in the knowledge base; and

for each identified pair of media items, adjusting a concurrency metric value responsive to each additional occurrence of the same pair of items in the knowledge base.

18. The non-transitory computer-readable storage medium of claim 17, wherein the concurrency metric value is a co-concurrency metric value, and adjusting comprises incrementing the co-concurrency metric value responsive to every additional occurrence of the same pair of items in the knowledge base.

19. The non-transitory computer-readable storage medium of claim 17, wherein the media items are ordered within the mediasets in the knowledge base, the instructions further comprising identifying comprises defining ordered pairs of items that appear in any mediaset in the knowledge base in the same order, and wherein the concurrency metric value is an ordered concurrency metric value, and adjusting comprises incrementing the ordered concurrency metric value responsive to every additional occurrence of the same ordered pair of items in the knowledge base.

20. The non-transitory computer-readable storage medium of claim 17, the instructions further comprising:

receiving, from a client device, at least one input media item;

identifying a plurality of ordered pairs containing the input media item; and

selecting one or more of the ordered pairs by evaluating the metric value.