Recommender system for identifying a new set of media items responsive to an input set of media items and knowledge base metrics

• US 8,312,017 B2
• Filed: 01/11/2010
• Issued: 11/13/2012
• 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
• Primary Examiner(s): ALAM, SHAHID AL
• Application Number:
  US12/685,639
• Publication Number:
  US 20100161595A1
• Time in Patent Office: 1,037 Days
• Field of Search: 707/722, 707/723, 707/791, 707/822, 707/828, 707/913, 707/736, 707/780, 709/217, 709/219, 709/242, 846/00
• US Class Current: 707/736
• 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 computer-implemented method for dynamically generating an output set of media items responsive to an input set of media items comprising:

receiving an input set comprising at least one input media item;

accessing a knowledge base comprising a plurality of collected mediasets, each knowledge base mediaset comprising at least one media item;

for an input media item, computing a corresponding value of a selected metric relative to each one of a plurality of media items in the knowledge base, wherein computing comprises computing a corresponding metric value for each input item relative to each one of a plurality of media items in the knowledge base by applying a co-concurrency metric responsive to a number of times that the respective input media item appears together with the corresponding media item in the same mediaset in the knowledge base;

responsive to the computed metric values, selecting at least one of the knowledge base media items as a candidate output media item;

selecting among the candidate output media items to form the output set of media items, wherein selecting a media item for which the computed metric value exceeds a predetermined threshold metric value; and

delivering the output set of media items to a user.

2. The method according to claim 1 wherein the input set of media items comprises first meta data identifying songs.

3. The method according to claim 2 wherein the output set of media items comprises second meta data identifying songs.

4. The method according to claim 1 wherein:

selecting among the candidate output media items to form the output set of media items comprises applying a predetermined selection criterion responsive to the media item distribution from which the candidate output media items were selected.

5. The method according to claim 1 wherein:

the input set of media items comprises first meta data identifying songs;

the knowledge base comprises a plurality of collected mediasets of songs; and

the output set of media items comprises second meta data identifying songs.

6. The method according to claim 5 wherein delivering the output set of media items to a user comprises displaying or transmitting the second meta data to the user.

7. The method according to claim 6 and further comprising filtering the selected candidate output media items to apply a predetermined additional external constraint to form the output set of media items.

8. The method according to claim 7 wherein filtering comprises maximizing the sum of the selected metric values among each pair of media items included in selecting the output set.

9. The method according to claim 7 wherein filtering comprises applying a predetermined preference or criterion selected by the user.

10. A method for dynamically generating an output set of media items responsive to an input set of media items comprising:

receiving an input set comprising at least one input media item;

accessing a knowledge base comprising a plurality of collected mediasets, each knowledge base mediaset comprising at least one media item;

for an input media item, computing a corresponding value of a selected metric relative to each one of a plurality of media items in the knowledge base, wherein computing comprises computing a corresponding metric value for each input item relative to each one of a plurality of media items in the knowledge base by applying a co-concurrency metric responsive to a number of times that the respective input media item appears together with the corresponding media item in the same mediaset in the knowledge base;

selecting among the knowledge base media items the items having the highest computed metric values as candidate output media items, wherein selecting a media item for which the computed metric value exceeds a predetermined threshold metric value; and

selecting among the candidate output media items to form the output set of media items.

11. The method according to claim 10 wherein the selected metric comprises one of pre-concurrency metric, a post-concurrency metric and a co-concurrency metric.

12. The method according to claim 10 wherein:

the input set of media items comprises first meta data identifying songs;

the knowledge base comprises a plurality of collected mediasets of songs; and

the output set of media items comprises second meta data identifying songs.

13. The method according to claim 12 and further comprising filtering the selected candidate output media items to form the output set of media items.

14. The method according to claim 13 wherein filtering applies a filter selected by a user to limit the output set of media items to a determined type of items.

15. The method according to claim 13 wherein filtering applies a filter selected by a user to limit the output set of media items to items that meet defined keyword-based meta data criteria, thereby personalizing the recommendations.

16. A computer-implemented method for dynamically generating an output set of media items responsive to an input set of media items comprising:

receiving an input set comprising at least one input media item from a first user;

accessing a knowledge base comprising a plurality of collected mediasets from a plurality of other users, each knowledge base mediaset comprising at least one media item;

for an input media item, computing a corresponding value of a selected metric relative to each one of a plurality of media items in the knowledge base, wherein computing comprises computing a corresponding metric value for each input item relative to each one of a plurality of media items in the knowledge base by applying a co-concurrency metric responsive to a number of times that the respective input media item appears together with the corresponding media item in the same mediaset in the knowledge base;

responsive to the computed metric values, selecting at least one of the knowledge base media items as a candidate output media item;

selecting among the candidate output media items to form the output set of media items, wherein selecting a media item for which the computed metric value exceeds a predetermined threshold metric value; and

delivering the output set of media items to the first user.

17. A computer-implemented method for dynamically generating an output set of media items responsive to an input set of media items comprising:

receiving an input set comprising at least one input media item;

accessing an ordered knowledge base comprising a plurality of collected mediasets, each knowledge base mediaset comprising at least one media item;

for an input media item, computing a corresponding value of a selected metric relative to each one of a plurality of media items in the knowledge base, wherein the step of computing includes computing a corresponding metric value for each input item relative to each one of a plurality of media items in the knowledge base by applying at least one of a pre-concurrency metric and post-concurrency metric;

responsive to the computed metric values, selecting at least one of the knowledge base media items as a candidate output media item;

selecting among the candidate output media items to form the output set of media items, wherein selecting a candidate output media item comprises selecting a media item for which the computed metric value exceeds a predetermined threshold metric value; and

delivering the output set of media items to a user.

18. A method for dynamically generating an output set of media items responsive to an input set of media items comprising:

receiving an input set comprising at least one input media item;

accessing an ordered knowledge base comprising a plurality of collected mediasets, each knowledge base mediaset comprising at least one media item;

for an input media item, computing a corresponding value of a selected metric relative to each one of a plurality of media items in the knowledge base, wherein the step of computing includes computing a corresponding metric value for each input item relative to each one of a plurality of media items in the knowledge base by applying at least one of a pre-concurrency metric and post-concurrency metric;

selecting among the knowledge base media items the items having the highest computed metric values as candidate output media items, wherein selecting a candidate output media item comprises selecting a media item for which the computed metric value exceeds a predetermined threshold metric value; and

selecting among the candidate output media items to form the output set of media items.