ADAPTIVE RECOMMENDER TECHNOLOGY

• US 20090300008A1
• Filed: 05/29/2009
• Published: 12/03/2009
• Est. Priority Date: 05/31/2008
• Status: Abandoned Application

• Current Assignee: Apple Inc.
• Original Assignee: Strands Incorporated (CRIBIS Holding SRL)
• Inventors: Shur, James, Hangartner, Rick
• Application Number:
  US12/475,220
• Publication Number:
  US 20090300008A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/4387 by the use of playlists
• CPC Class Codes: G11B 27/105 of operating discs

Abstract

A computer implemented method for incorporating media item data for use in a media item recommender system comprising: accessing a first database comprising a plurality of media item identifiers and associated metadata corresponding to each of a plurality of media items identified by the media item identifiers; generating first correlation data based on a comparison of the metadata corresponding to pairs of the media item identifiers to detect similarities between the media items identified; accessing a second database comprising a plurality of media item identifier sets for identifying sets of media items; generating second correlation data based on an analysis of the media item identifier sets to determine incidence of selected subsets of media item identifiers occurring together in a same media item identifier set; accessing a third database comprising a plurality of consumed media item identifier sets, wherein the consumed media item identifier sets associate one or more media item identifiers in a particular set based on media item consumption data; generating third correlation data based on an analysis of the consumed media item identifier sets to determine incidence of selected subsets of the consumed media item identifiers occurring together in a same consumed media item identifier set; and merging the first, second, and third correlation data to generate media item recommender data.

Claims

1. A computer implemented method for incorporating media item data for use in a media item recommender system, the method comprising:

accessing a first database comprising a plurality of media item identifiers and associated metadata corresponding to each of a plurality of media items identified by the media item identifiers;

generating first correlation data based on a comparison of the metadata corresponding to pairs of the media item identifiers to detect similarities between the media items identified;

accessing a second database comprising a plurality of media item identifier sets for identifying sets of media items;

generating second correlation data based on an analysis of the media item identifier sets to determine incidence of selected subsets of media item identifiers occurring together in a same media item identifier set;

accessing a third database comprising a plurality of consumed media item identifier sets, wherein the consumed media item identifier sets comprise associated one or more media item identifiers corresponding to media item consumption data;

generating third correlation data based on an analysis of the consumed media item identifier sets to determine incidence of selected subsets of the consumed media item identifiers occurring together in a same consumed media item identifier set; and

merging the first, second, and third correlation data to generate media item recommender data.

2. The computer implemented method according to claim 1 further comprising:

generating media item recommendations for user consumption during a user session based on the media item recommender data, wherein the user session includes presentation of at least one pair of media items;

accessing user session data, wherein the user session data corresponds to user feedback characterizing user reactions to the presentation of recommended media items;

analyzing the user session data for an individual media item of the pair and for the pair of media items to form user feedback statistics; and

modifying the media item recommender data based on the user feedback statistics to generate tuned media item recommender data.

3. The computer implemented method according to claim 2, wherein the user session data comprises data reflecting a plurality of media sessions among a defined audience of users.

4. The computer implemented method according to claim 1, further comprising decreasing a contribution of the first correlation data to the media item recommender data over a time period relative to the contribution of second and third correlation data to the media item recommender data.

5. The computer implemented method according to claim 1, wherein merging the first, second, and third correlation data further comprises:

combining the second and third correlation data together to generate a preliminary recommender dataset; and

adding the preliminary recommender dataset together with the first correlation data to generate the media item recommender data.

6. The computer implemented method according to claim 5, wherein combining the second and third correlation data together further comprises:

estimating a probability of association for pairs of media items identified in the second and third correlation data to generate an association dataset based on similarity; and

generating the preliminary recommender dataset based on relationships between the media items in the association dataset.

7. The computer implemented method according to claim 6, further comprising a graph search of the first association dataset comprising:

generating a first graph corresponding to the first association dataset comprising first nodes and first edges, wherein each node represents a media item and each edge represents the second or third correlation data, or combinations thereof;

searching the first graph to identify and characterize paths between connected nodes; and

generating a second graph comprising second nodes associated with the first nodes and further comprising second weighted edges connecting pairs of second nodes wherein the second weighted edges correspond to the paths identified in the first graph.

8. The computer implemented method according to claim 7, wherein the second weighted edges correspond to similarity or distance, or combinations thereof between the media items connected by the second weighted edges.

9. The computer implemented method according to claim 8, further comprising generating a third graph comprising third nodes and third weighted edges,

wherein the third nodes correspond to the plurality of media items,

wherein every third node is connected to every other third node in the third graph, and wherein the third weighted edges correspond to the similarity between the connected third nodes based on the first correlation data.

10. The computer implemented method according to claim 9, wherein merging the first, second, and third correlation data to generate media item recommender data further comprises combining the second and third graphs.

11. The computer implemented method according to claim 6, wherein if there are media item identifiers in the first database that do not appear in the second or third databases then combining the preliminary recommender dataset with the third correlation data.

12. The computer implemented method according to claim 2, wherein the user feedback corresponds to media item plays, skips, repeats, negative user evaluation, neutral user evaluation, or positive user evaluation, or combinations thereof.

13. The computer implemented method according to claim 2, wherein analyzing of the user session data to form user feedback statistics occurs at predetermined time intervals.

14. The method according to claim 2, wherein modifying the media item recommender data based on the user feedback statistics further comprises:

generating a first graph comprising a first plurality of media item identifiers connected at least in pairs via first edges, the first edges corresponding to the second and third correlation data;

generating a second graph comprising the first plurality of media item identifiers connected via second weighted edges, the second weighted edges connecting all pairs of media items identifiers for which a connecting path exists in the first graph, wherein the second weighted edges correspond to a similarity metric between media items based on the first graph;

generating a third graph comprising a second plurality of media item identifiers comprising at least one media item identifier not present in the first plurality of media item identifiers, wherein pairs of media item identifiers are connected via third weighted edges, wherein the third weighted edges correspond to the similarity between the connected media items based on the first correlation data;

generating a fourth graph comprising a third plurality of media item identifiers connected via fourth weighted edges, wherein the fourth weighted edges correspond to the similarity between the connected media items based on the user feedback statistics;

combining the first, second, third, and fourth graphs to generate the tuned media item recommender data.

15. The computer implemented method according to claim 2, wherein modifying the media item recommender data based on the user feedback statistics further comprises:

generating a first data structure representing co-occurrence estimation data corresponding to the second and third correlation data;

generating a second data structure representing similarity data based on the co-occurrence data of the first data structure;

generating a third data structure representing similarity data corresponding to the first correlation data;

generating a fourth data structure representing similarity data corresponding to the feedback statistics;

combining the first, second, third, and fourth data structures to generate the generate tuned media item recommender data.

16. The computer implemented method of claim 1, further comprising generating the database of consumed media item identifier sets by segmenting media items played by users according to predetermined segmenting criteria and storing media items played during a same segment as a single consumed media item set.

17. The computer implemented method of claim 16, wherein the predetermined segmenting criteria comprises a change in two or more of the following:

client identification, originating IP address for a play event, offset from GMT for client local time, the two-letter ISO country code returned by GeoIP for the IP address, media play shuffle mode flag, source of play event track, text name of particular source of play event, or name of playlist retuned by music player.

18. A computer implemented method for incorporating media item data for use in a media item recommender system, the method comprising:

accessing a catalog of media item identifiers and associated metadata;

analyzing the metadata to form first association data correlating at least a some of the media items in the catalog;

accessing a catalog of media item identifier sets;

analyzing the media item identifier sets to form second association data corresponding to subsets of media item identifiers occurring in the media item identifier sets;

accessing a catalog of consumed media item identifier sets, wherein the consumed media item identifier sets are grouped based on media consumption data;

analyzing the consumed media item identifier sets to form third association data corresponding to subsets of media item identifiers occurring in the consumed media item identifier sets; and

merging the first, second, and third association data to generate media item identifier recommender data.

19. The computer implemented method for incorporating user feedback according to claim 18 further comprising:

accessing user session data, wherein the user session data is based on user feedback characterizing user reactions to a presentation of recommended media items;

analyzing the user session data to quantify user feedback data for an individual media item of a pair of media items presented during the user session and for the pair of media items to form user feedback statistics; and

modifying the media item recommender data based on the user feedback statistics to generate tuned media item recommender data.

20. The computer implemented method according to claim 18, wherein a contribution of first association data decreases over a time period as a contribution of second and third association data increases over the time period.

21. A system for driving a recommender datastore-based application program, comprising:

a playlist datastore storing a dataset of playlists of media items;

a playstream datastore storing a dataset of playstreams of media items, reflecting user interactions with media items;

a metadata datastore storing a dataset of media catalogs comprising metadata of media items;

a user feedback datastore storing user feedback data generated in response to user interaction events corresponding to presentation of media items to users via the application program;

a processor arranged for combining the playlist dataset, the playstream dataset, the metadata dataset and the user feedback data to form a new dataset of media items; and

a recommender datastore for storing the new dataset and providing access for the application to access the new dataset.