Methods and apparatus for generating recommendation scores

• US 20020174429A1
• Filed: 03/29/2001
• Published: 11/21/2002
• Est. Priority Date: 03/29/2001
• Status: Abandoned Application

• Current Assignee: Trident Microsystems Limited (Avnet Incorporated)
• Original Assignee: Trident Microsystems Limited (Avnet Incorporated)
• Inventors: Gutta, Srinivas, Kurapati, Kaushal, Schaffer, David J.
• Application Number:
  US09/821,277
• Publication Number:
  US 20020174429A1
• Time in Patent Office: Days
• US Class Current: 725/46
• CPC Class Codes: H04N 21/44222 Analytics of user selections, e.g. selection of programs or purchase activity monitoring of user selections in data processing systems G06F11/34; arrangements for monitoring the user's behaviour or opinions in broadcast systems H04H60/33
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; 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 21/454 Content or additional data filtering, e.g. blocking advertisements
• 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 21/4662 characterized by learning algorithms
• 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 21/47 End-user applications
• 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 7/163 by receiver means only

Abstract

Methods and apparatuses for recommending television programs are provided. The methods provided include obtaining a list of one or more television programs to at least three different program recommenders, obtaining from each recommender a recommendation score, and computing a combined recommendation score by applying a voting process. The combined recommendation score is then presented to a user, who, based thereon, can select a television program of interest. The voting process is a stochastic method including a Bayesian method, a hierarchical decision tree, a memory based learning process, a rule based learning process, a neural network or a hidden markov model. The enumerated stochastic processes can be further combined according to a combination scheme including a unison scheme, a majority scheme, a trust scheme, an averaging scheme or mixture thereof.

Claims

1. A method for recommending television programs, comprising:

obtaining a list of one or more television programs;

providing said list of programs to at least three different program recommenders, R₁, R₂ and R₃;

obtaining for each program on said list a set of recommendation scores, S₁, S₂ and S₃, from each of said recommenders, R₁, R₂ and R₃;

generating for each program on said list a combined recommendation score, C, computed by applying a voting process to each said recommendation scores S₁, S₂ and S₃; and

recommending the program to a user by presenting said combined recommendation score, C, to said user.

2. The method of claim 1, wherein said recommendation scores S₁, S₂ and S₃ are implicit recommendation scores I₁, I₂ and I₃ for said one or more programs.

3. The method of claim 2, wherein said voting process is based on a stochastic method.

4. The method of claim 3, wherein said stochastic method comprises a Bayesian method, a hierarchical decision tree method, a memory based learning process, a rule based learning process, a neural network or a hidden markov model.

5. The method of claim 4, wherein said stochastic methods are combined according to a combination scheme comprising a unison scheme, a majority scheme, a trust scheme, an averaging scheme or mixtures thereof.

6. The method of claim 1, wherein said combined recommendation score, C, enables the user to select a television program of interest.

7. The method of claim 2, further comprising generating at least an explicit recommendation score, E, for said one or more television programs;

and

generating a combined recommendation score, C_e, computed by applying a voting process to each of said implicit recommendation scores and said explicit recommendation score, E.

8. The method of claim 7, further comprising generating at least a feedback score F, for said one or more television programs;

and

generating a combined recommendation score, C_f, computed by applying a voting process to each of said implicit recommendation scores, said explicit recommendation score and said feedback score.

9. The method of claim 8, wherein said voting process is based on a stochastic method.

10. The method of claim 9, wherein said stochastic method comprises a Bayesian method, a hierarchical decision tree method, a memory based learning process, a rule based learning process, a neural network or a hidden markov model.

11. The method of claim 10, wherein said stochastic methods are combined according to a combination scheme comprising a unison scheme, a majority scheme, a trust scheme, an averaging scheme or a mixture thereof.

13. The method of claim 12, wherein said voting process is based on a stochastic process.

14. The method of claim 13, wherein said process comprises a Bayesian method, a hierarchical decision tree method, a memory based learning process, a rule based learning process, a neural network or a hidden markov model.

15. The method of claim 14, wherein said stochastic processes are combined according to a combination scheme comprising a unison scheme, a majority scheme, a trust scheme, an averaging scheme or a mixture thereof.

16. The, method of claim 12, wherein said combined recommendation score, C, enables said user to select a television program of interest.

18. The system of claim 17, wherein said voting process is based on a stochastic method comprising a Bayesian method, a hierarchical decision tree method, a memory based learning process, a rule based learning process, a neural network or a hidden markov model.

19. The system of claim 17, wherein said stochastic processes are combined according to a combination scheme comprising a unison scheme, a majority scheme, a trust scheme, an averaging scheme, or a mixture thereof.

12. A method for recommending television programs, comprising:

obtaining a list of one or more television programs;

obtaining at least an explicit recommendation score, E, for said one or more television programs;

obtaining at least an implicit recommendation score, I, for said one or more television programs;

obtaining at least a feedback recommendation score, F, for said one or more television programs;

generating for each television program a combined recommendation score, C, based on applying a voting process to each said explicit recommendation score, said implicit recommendation score and said feedback recommendation score; and

recommending said combined recommendation score, C, to a user by presenting said combined recommendation score, C, to said user.

17. A system for obtaining a recommendation for a television program for a user, said system comprising:

a memory for storing computer readable code; and

a processor operatively coupled to said memory, said processor configured to;

obtain a list of one or more television programs;

provide said list of television programs to at least three television program recommenders, R₁, R₂ and R₃;

obtain for each television program on said list a set of recommendation scores, S₁, S₂ and S₃ from each of said recommenders, R₁, R₂ and R₃;

generate for each television program on said list a combined recommendation score, C, computed by applying a voting process to each of said recommendation scores S₁, S₂ and S₃; and

recommending said combined recommendation score, C, by presenting said combined recommendation score, C, to a user.

20. A system for obtaining a recommendation for a television program for a user which comprises:

a memory for storing computer readable code; and

a processor operatively coupled to said memory, said processor configured to;

obtain a list of one or more television programs;

obtain at least an explicit recommendation score, E, for said one or more television programs;

obtain at least an implicit recommendation score, I, for said one or more television programs;

obtain at least a feedback recommendation score, F, for said one or more television programs;

generate a combined recommendation score, C, based on applying a voting process to each said explicit recommendation score, said implicit recommendation score and said feedback recommendation score; and

recommend said combined recommendation score, C, to a user.

21. The, system of claim 20, wherein said voting process is based on a stochastic method comprising a Bayesian method, a hierarchical decision tree method, a memory based learning process, a rule based learning process, a neural network or a hidden markov model.

22. The system of claim 21, wherein said stochastic processes are combined according to a combination scheme comprising a unison scheme, a majority scheme, a trust scheme, an averaging scheme, or a mixture thereof.