Four-way recommendation method and system including collaborative filtering

• US 20030051240A1
• Filed: 09/10/2001
• Published: 03/13/2003
• Est. Priority Date: 09/10/2001
• Status: Abandoned Application

• Current Assignee: Pace Micro Technology PLC (CommScope Holding Company, Inc.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Schaffer, J. David, Gutta, Srinivas, Kurapati, Kaushal
• Application Number:
  US09/953,385
• Publication Number:
  US 20030051240A1
• Time in Patent Office: Days
• US Class Current: 725/34
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• 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 21/44224 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 21/4661 Deriving a combined profile for a plurality of end-users of the same client, e.g. for family members within a home user profiles in network data switching protocols H04L67/306
• CPC Class Codes: H04N 21/4663 involving probabilistic networks, e.g. Bayesian networks
• CPC Class Codes: H04N 21/4665 involving classification methods, e.g. Decision trees
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/4755 for defining user preferences, e.g. favourite actors or genre retrieval personalisation and generation of user profiles for the retrieval of video data G06F16/739; user profiles in network data switching protocols H04L67/306
• CPC Class Codes: H04N 21/4756 for rating content, e.g. scoring a recommended movie
• CPC Class Codes: H04N 7/17318 Direct or substantially direct transmission and handling of requests

Abstract

A system employing an automated collaborative filtering process for recommending an item to a viewer based upon feedback data, implicit data, and/or explicit data corresponding to a primary viewer as well as secondary viewers is disclosed. A first act of the automated collaborative filtering process is to match data indicative of a viewing of a first group of items by the primary viewer to data indicative of a viewing of a second group of items by the secondary viewers. A second act of the automated collaborative filtering process is to generate a recommendation of the item by the primary viewer as a function of data indicative of one or more attributes of the item as compared to the data matching accomplished in the first act.

Claims

1. An automated collaborative filtering method for providing a recommendation of a first item to a primary viewer, said method comprising:

matching a first data to a subset of a second data, the first data indicative of a viewing of a first group of items by the primary viewer, the second data indicative of a viewing of a second group of items by a first group of secondary viewers; and

generating the recommendation of the first item as a function of a third data and the subset of the second data, the third data indicative of one or more attributes of the first item.

2. The automated collaborative filtering method of claim 1, wherein:

the first data includes a feedback viewing profile of the primary viewer; and

the second data includes a feedback viewing profile of each viewer of the first group of secondary viewers.

3. The automated collaborative filtering method of claim 1, wherein:

the first data includes a feedback viewing history of the primary viewer; and

the second data includes a feedback viewing history of each viewer of the first group of secondary viewers.

4. The automated collaborative filtering method of claim 1, wherein:

the first data includes an implicit viewing profile of the primary viewer; and

the second data includes an implicit viewing profile of each viewer of the first group of secondary viewers.

5. The automated collaborative filtering method of claim 1, wherein:

the first data includes an implicit viewing history of the primary viewer; and

the second data includes an implicit viewing history of each viewer of the first group of secondary viewers.

6. The automated collaborative filtering method of claim 1, wherein:

the first data includes an explicit viewing profile of the primary viewer; and

the second data includes an explicit viewing profile of each viewer of the first group of secondary viewers.

7. The automated collaborative filtering method of claim 1, further comprising:

matching the first data to a subset of a fourth data, the fourth data indicative of a viewing of a third group of items by a second group of secondary viewers;

wherein the recommendation of the first item is generated as a function of the third data, the subset of the second data, and the subset of the fourth data.

8. The automated collaborative filtering method of claim 7, further comprising:

matching the first data to a subset of a fifth data, the fifth data indicative of a viewing of a fourth group of items by a third group of secondary viewers,

wherein the recommendation of the first item is generated as a function of the third data, the subset of the second data, the subset of the fourth data, and the subset of the fifth data.

9. An automated collaborative filtering system for providing a recommendation of a first item to a primary viewer, said system comprising:

a first module for matching the first data to a subset of the second data, the first data being indicative of a viewing of a first group of items by the primary viewer, the second data being indicative of a viewing of a second group of items by the first group of secondary viewers; and

a second module for generating the recommendation of the first item as a function of a third data and the subset of the second data,

wherein the third data is indicative of one or more attributes of the first item.

10. The automated collaborative filtering system of claim 9, wherein:

the first data includes a feedback viewing profile of the primary viewer; and

the second data includes a feedback viewing profile of each viewer of the first group of secondary viewers.

11. The automated collaborative filtering system of claim 9, wherein:

the first data includes a feedback viewing history of the primary viewer; and

the second data includes a feedback viewing history of each viewer of the first group of secondary viewers.

12. The automated collaborative filtering system of claim 9, wherein:

the first data includes an implicit viewing profile of the primary viewer; and

the second data includes an implicit viewing profile of each viewer of the first group of secondary viewers.

13. The automated collaborative filtering system of claim 9, wherein:

the first data includes an implicit viewing history of the primary viewer; and

the second data includes an implicit viewing history of each viewer of the first group of secondary viewers.

14. The automated collaborative filtering system of claim 9, wherein:

the first data includes an explicit viewing profile of the primary viewer; and

the second data includes an explicit viewing profile of each viewer of the first group of secondary viewers.

15. The automated collaborative filtering system of claim 9, further comprising:

a third module for matching the first data to a subset of a fourth data, the fourth data being indicative of a viewing of a third group of items by a second group of secondary viewers,

wherein said second module is operable to generate the recommendation of the first item as a function of the third data, the subset of the second data, and the subset of the fourth data.

16. The automated collaborative filtering system of claim 15, further comprising:

a fourth module for matching the first data to a subset of a fifth data, the fifth data being indicative of a viewing of a fourth group of items by a third group of secondary viewers,

wherein said second module is operable to generate the recommendation of the first item as a function of the third data, the subset of the second data, the subset of the fourth data, and the subset of the fifth data.

17. Computer program product in a computer readable medium for providing a recommendation of a first item to a primary viewer, said computer program product comprising:

a first computer readable code for matching a first data to a subset of a second data, the first data being indicative of a viewing of a first group of items by the primary viewer, the second data being indicative of a viewing of a second group of items by a first group of secondary viewers; and

a second computer readable code for generating the recommendation of the first item as a function of a third data and the subset of the second data,

wherein the third being data is indicative of one or more attributes of the first item.

18. The computer readable product of claim 17, wherein:

the first data includes a feedback viewing profile of the primary viewer; and

the second data includes a feedback viewing profile of each viewer of the first group of secondary viewers.

19. The computer readable product of claim 17, wherein:

the first data includes a feedback viewing history of the primary viewer; and

the second data includes a feedback viewing history of each viewer of the first group of secondary viewers.

20. The computer readable product of claim 17, wherein:

the first data includes an implicit viewing profile of the primary viewer; and

the second data includes an implicit viewing profile of each viewer of the first group of secondary viewers.

21. The computer readable product of claim 17, wherein:

the first data includes an implicit viewing history of the primary viewer; and

the second data includes an implicit viewing history of each viewer of the first group of secondary viewers.

22. The computer readable product of claim 17, wherein:

the first data includes an explicit viewing profile of the primary viewer; and

the second data includes an explicit viewing profile of each viewer of the first group of secondary viewers.

23. The computer readable product of claim 17, further comprising:

a third computer readable code for matching the first data to a subset of a fourth data, the fourth data being indicative of a viewing of a third group of items by a second group of secondary viewers,

wherein said second computer readable code is for generating the recommendation of the first item as a function of the third data, the subset of the second data, and the subset of the fourth data.

24. The computer readable product of claim 23, further comprising:

a fourth computer readable code for matching the first data to a subset of a fifth data, the fifth data being indicative of a viewing of a fourth group of items by a third group of secondary viewers,

wherein said second computer readable code is for generating the recommendation of the first item as a function of the third data, the subset of the second data, the subset of the fourth data, and the subset of the fifth data.

25. An automated collaborative filtering system for providing a recommendation of an item to a primary viewer, said system comprising:

means for matching a first data to a subset of the second data, the first data being indicative of a viewing of a group of items by the primary viewer, the second data being indicative of a viewing of a second group of items by the group of secondary viewers; and

means for generating the recommendation of the item as a function of a third data and the subset of the second data,

wherein the third data being indicative of one or more attributes of the item.