RECOMMENDATION ENGINE APPARATUS AND METHODS

• US 20100251305A1
• Filed: 03/30/2009
• Published: 09/30/2010
• Est. Priority Date: 03/30/2009
• Status: Active Grant

• Current Assignee: Time Warner Cable Enterprises LLC (Charter Communications Incorporated)
• Original Assignee: Time Warner Cable Enterprises LLC (Charter Communications Incorporated)
• Inventors: Kimble, Dave, Chin, Fee Ling
• Granted Patent: US 9,215,423 B2
• Time in Patent Office: Days
• US Class Current: 725/46
• CPC Class Codes: H04N 21/25891 being end-user preferences retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/2668 Creating a channel for a dedicated end-user group, e.g. insertion of targeted commercials based on end-user profiles information retrieval from the Internet by querying with filtering and personalisation G06F16/9535; arrangements for replacing or switching information during the broadcast H04H20/10; push services over packet-switching network H04L12/1859; adaptation of message content in packet-switching networks H04L51/063
• CPC Class Codes: H04N 21/44224 Monitoring of user activity on external systems, e.g. Internet browsing
• 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 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/4826 using recommendation lists, e.g. of programs or channels sorted out according to their score
• CPC Class Codes: H04N 21/6582 Data stored in the client, e.g. viewing habits, hardware capabilities, credit card number arrangements where receivers interact with the broadcast H04H20/38
• CPC Class Codes: H04N 7/17318 Direct or substantially direct transmission and handling of requests

Abstract

Recommendation engine apparatus and associated methods provide content compiled from various sources and selected to match user preferences. In one embodiment, the recommendation apparatus comprises a headend entity; in another, it is co-located on a user'"'"'s CPE. In one embodiment, the recommendation engine creates content records from content metadata for comparison to a user profile. The user profile is pre-programmed; however has the ability to dynamically shift toward a user'"'"'s preferences as the user takes actions regarding content. Client applications are utilized to compile and present content; feedback mechanisms are utilized to enable “learning” from user activities to generate more precise recommendations as well as to “unlearn” stale preferences. Recommended content is displayed in the form of a playlist, or as a continuous stream on a virtual channel, or presented in an electronic program guide. A business rules “engine” useful in implementing operational or business goals is also disclosed.

Claims

1. For use in a content based network, recommendation apparatus comprising:

a storage device, said storage device adapted to;

store a plurality of information regarding a plurality of content; and

store a plurality of user profiles; and

a digital processor in data communication with said storage device and adapted to run a computer program thereon, said computer program adapted to;

compare said information regarding a plurality of content to individual ones of said plurality of user profiles; and

based at least in part on said comparison, produce at least one list of content, said content bearing a relationship to at least one of said plurality of user profiles; and

an interface in data communication with said network and said processor.

2. The recommendation apparatus of claim 1, wherein said apparatus comprises a headend entity of said network.

3. The recommendation apparatus of claim 1, wherein said recommendation apparatus comprises a hub entity of said network.

4. The recommendation apparatus of claim 1, wherein said recommendation apparatus comprises a consumer premises device.

5. The recommendation apparatus of claim 1, wherein said plurality of information regarding said plurality of content and said plurality of user profiles are respectively expressed as first vectors and second vectors having identical numbers of columns and rows as one another, said first vectors and second vectors comprising one or more aspects corresponding to metadata associated with individual ones of said plurality of content.

6. The recommendation apparatus of claim 5, wherein said plurality of information regarding said plurality of content and said plurality of user profiles are expressed as n×

1 column vectors and said storage device is further adapted to store a n×

1 column reference vector defining aspects expressed in said vectors.

7. The recommendation apparatus of claim 5, wherein said plurality of information regarding said plurality of content and said plurality of user profiles are expressed as n×

2 column vectors.

8. The recommendation apparatus of claim 5, wherein said plurality of information regarding said plurality of content and said plurality of user profiles are expressed as 1×

n row vectors.

9. The recommendation apparatus of claim 5, wherein said act of comparing said plurality of content to said plurality of user profiles comprises:

generating a content record for individual ones of said plurality of content, said content records comprising a vector containing metadata regarding said content; and

calculating a dot product of individual ones of said first vectors and individual ones of said second vectors to produce a scalar quantity.

10. The recommendation apparatus of claim 9, wherein said act of producing at least one list of content comprises creating a list of content having a scalar quantity greater than or equal to a pre-set real-value number, and providing said list to a client device associated with said at least one user profile.

11. The recommendation apparatus of claim 1, wherein said list comprises information for identification and retrieval of each of said content in said list.

12. The recommendation apparatus of claim 1, wherein at least one of said plurality of user profiles comprises a template user profile.

13. The recommendation apparatus of claim 5, wherein said program is further adapted to update said at least one user profile by adding a training vector to individual ones of said second vectors, said training vector comprising weighted data regarding one or more user actions.

14. Computer readable apparatus comprising media adapted to contain a computer program having a plurality of instructions, said plurality of instructions which, when executed:

examine metadata associated with a plurality of content;

generate a content record for each of said plurality of content examined based at least in part on said metadata;

compare individual ones of said content records to individual ones of a plurality of user profiles associated with individual ones of a plurality of client devices;

compile at least one list, said list comprising information regarding individual ones of said plurality of content having a threshold similarity to at least one of said plurality of user profiles, and information for identification and retrieval of each of said content in said list; and

transmit said list for display by at least one of said plurality of client devices.

15. The apparatus of claim 14, wherein said metadata comprises information regarding at least one of genre, content type, advisory rating, language, era, or actor.

16. The apparatus of claim 14, wherein said content records and said plurality of user profiles are expressed as vectors having identical numbers of columns and rows as one another, said vectors comprising one or more aspects corresponding to metadata associated with individual ones of said plurality of content.

17. The apparatus of claim 16, wherein said act of comparing said content records to said plurality of user profiles comprises calculating a dot product of said content record vectors and said user profile vectors to produce a scalar quantity.

18. The apparatus of claim 17, wherein said act of compiling at least one list of content having a threshold similarity to at least one of said plurality of user profiles comprises creating a list of content having a scalar quantity with respect to said user profile greater than or equal to a predetermined real-value number.

19. The apparatus of claim 16, wherein said computer program is further adapted to update at least one of said plurality of user profiles according to user actions occurring on a client device to which said at least one user profile is associated by adding a training vector to said user profile vector, said training vector comprising content records weighted according to user actions associated therewith.

20. A method of recommending content targeted to a particular user in a content based network, said method comprising:

generating a plurality of content records regarding a plurality of content;

comparing individual ones of said plurality of content records to at least one user profile;

storing information regarding individual ones of said plurality of content records which bear a substantial relation to said user profile; and

displaying said information regarding said individual ones of said plurality of content records.

21. The method of claim 20, wherein said act of generating said plurality of content records comprises utilizing metadata associated with said plurality of content, and wherein said content records and said user profile are expressed as vectors having identical numbers of columns and rows as one another.

22. The method of claim 21, wherein said act of comparing individual ones of said plurality of content records to said at least one user profile comprises calculating a dot product of individual ones of said content records to said user profile to produce a scalar quantity.

23. The method of claim 22, wherein said act of displaying said information comprises displaying said information in a list which is arranged based at least in part by said scalar quantity.

24. The method of claim 20, further comprising providing content associated with at least one of said individual ones of said plurality of content records which bear a substantial relation to said user profile.

25. The method of claim 20, wherein determination of said content records bearing substantial relation to said user profile comprises examining various aspects of said content records against corresponding aspects of said user profile.

26. A user action tracking apparatus, said user action tracking apparatus adapted for data communication with at least a computer program adapted to recommend content to a user, said user action tracking apparatus comprising:

an interface for receiving user action data from a content-based network;

a storage apparatus adapted to store a plurality of records regarding user actions, each record relating at least one action of a user to an individual content element; and

a digital processor adapted to run a computer program thereon, said computer program adapted to;

generate said plurality of records regarding user actions from said user action data;

utilize said plurality of records to generate a first training record; and

update said first training record for subsequent ones of said user action data received.

27. The tracking apparatus of claim 26, wherein said storage apparatus comprises a cache memory.

28. The tracking apparatus of claim 26, wherein said records regarding user actions comprise at least:

(i) identifying information regarding content to which said user action relates, (ii) a chronological reference, and (iii) a description or descriptor of said user action.

29. The tracking apparatus of claim 26, wherein said act of generating said first training record comprises:

generating a content record for each of said individual content to which said individual user actions relate, said content record comprising a vector having an identical number of columns and rows as a user profile associated with said user action;

associating said description of said user action associated with said individual content in said record regarding user actions with a weighting factor; and

multiplying said content record vector by said weighting factor.

30. The tracking apparatus of claim 29, wherein said act of updating said first training record comprises:

associating said description of said user action associated with said individual content in said record regarding user actions with a weighting factor; and

multiplying said first training record by said weighting factor to arrive at an updated training record.

31. The tracking apparatus of claim 26, wherein said apparatus is further adapted to distribute said updated training record to said computer program adapted to recommend content to a user for use thereby.

32. The tracking apparatus of claim 26, wherein said apparatus is further adapted to:

receive at least one user profile, said user profile represented as a vector; and

utilize said updated training record to update said user profile by adding said training record to said profile;

wherein said user profile and said training record comprise vectors having an identical number of rows and columns as one another.

33. A method of identifying content targeted to a user based on one or more actions of said user, said method comprising:

maintaining a user profile expressed using a vector;

generating a plurality of content records based, at least in part, on metadata regarding a plurality of content, said content records expressed as vectors, wherein said user profile vector and said content records vectors have identical numbers of columns and rows as the other;

providing access to said plurality of content to said user;

storing a record of at least one action taken by said user with respect to an individual one of said plurality of content;

generating a weighted content record of said content acted upon by said user;

utilizing said weighted content record at least in part to generate an updated user profile;

calculating a product of individual ones of said content records to said user profile to produce a scalar quantity; and

identifying individual ones of said plurality of content having a prescribed relation to said updated user profile based at least in part on said scalar quantity.

34. The method of claim 33, wherein said user profile comprises a pre-set or template user profile.

35. The method of claim 33, wherein said plurality of content records and said user profile are expressed as n×

1 column vectors; and

said method further comprises storing a n×

1 column reference vector defining aspects expressed in said vectors.

36. The method of claim 33, wherein said act of generating said weighted content record comprises multiplying said content record associated with said at least one act by a weighting factor.

37. The method of claim 33, wherein said act of identifying individual ones of said plurality of content having a prescribed relation to said user profile based at least in part on said scalar quantity comprises identifying individual ones of said plurality of content having a scalar quantity equal to or greater than a predetermined value.

38. The method of claim 33, further comprising displaying identifying information regarding said identified individual ones of said plurality of content having a prescribed relation to said user profile.

39. Computer readable apparatus comprising media adapted to contain a computer program having a plurality of instructions, said plurality of instructions which, when executed:

maintain at least one user profile;

generate a plurality of content records at least in part by utilizing metadata relating to a plurality of content;

provide access to said plurality of content to a user;

store a record of at least one act taken by said user with respect to an individual one of said plurality of content;

utilize said record of at least one act taken by said user, at least in part, to generate an updated user profile; and

compare said updated user profile to individual ones of said content records of said plurality of content to identify individual ones of said plurality of content having an prescribed level of relatedness to said user profile.

40. The computer readable apparatus of claim 39, wherein said user profile comprises a pre-set or template user profile.

41. The computer readable apparatus of claim 39, wherein said plurality of content records and said user profile are expressed as vectors having identical numbers of columns and rows as one another.

42. The computer readable apparatus of claim 41, wherein said act of comparing said updated user profile to individual ones of said content records comprises calculating a dot product of individual ones of said content records to said user profile to produce a scalar quantity.

43. The computer readable apparatus of claim 42, wherein said act of identifying individual ones of said plurality of content having a prescribed level of relatedness to said user profile comprises identifying individual ones of said plurality of content having a scalar quantity equal to or greater than a predetermined value.

44. The computer readable apparatus of claim 39, further adapted to:

generate a weighted content record of said content acted upon by multiplying said content record associated with said at least one act by a weighting factor determined by the nature of said act and an estimated relation of said act to said user'"'"'s preferences; and

add said weighted content record to said user profile to generate said updated user profile.

45. The computer readable apparatus of claim 39, wherein said act of identifying individual ones of said plurality of content having an predetermined level of relatedness to said user profile comprises examining attributes of individual ones of said plurality of content for comparison to equivalent attributes of said user profile.