Server System and Method for Network-Based Service Recommendation Enhancement

• US 20120203723A1
• Filed: 02/04/2011
• Published: 08/09/2012
• Est. Priority Date: 02/04/2011
• Status: Abandoned Application

• Current Assignee: Telefonaktiebolaget LM Ericsson
• Original Assignee: Telefonaktiebolaget LM Ericsson
• Inventors: Huang, Vincent, Söderberg, Joakim, Bararsani, Azadeh
• Application Number:
  US13/021,148
• Publication Number:
  US 20120203723A1
• Time in Patent Office: Days
• US Class Current: 706/20
• CPC Class Codes: G06Q 30/0278 Product appraisal
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A networked server system for enabling, facilitating and accuracy enhancing personalized service recommendations to a user of a new service S_N, comprising an abstract user profile database, a service transformation database, an input/output network interface, and a processing unit adapted and configured to provide user selection functionality, dimension reduction functionality, profile update functionality and service recommendation functionality. The server system is adapted and configured to:

• • receive, via a network, from a new service S_N, a service specific set of user profiles, denoted [P_N]∀ U_N and having a user dimension and an attribute classification category dimension;
  • combine the service specific set of user profiles [P_N]∀ U_N and a set of previously received set of user profiles, denoted [P_N−1]∀ U_N−1, or a derivative thereof, into a combined set, denoted [cP_N]∀ I_N, of user profiles for a set of users I_N=U_N∩U_N−1;
  • orthogonally transform [cP_N]∀ I_N into a set of abstract user profiles, denoted [aP_N] ∀ I_N, that are minimized in attribute classification dimension; and
  • reduce, in the attribute classification dimension, the abstract set [aP_N]]∀ I_N to an abstract reduced set, denoted [arP_N] ∀ I_N, of the user attributes having the highest variance; thereby enabling enhanced personalized.

Claims

1. A networked server system for enabling, facilitating and accuracy enhancing personalized service recommendations to a user using a new service, denoted S_N, comprising an abstract user profile database, a service transformation database, an input/output network interface, and a processing unit adapted and configured to provide user selection functionality, dimension reduction functionality, profile update functionality and service recommendation functionality, the system adapted and configured to:

receive, via a network, from a new service S_N, a service specific set of user profiles, denoted [P_N]∀

U_N and having a user dimension and an attribute classification category dimension;

combine the service specific set of user profiles [P_N]∀

U_N and a set of previously received set of user profiles, denoted [P<sub>N−

1</sub>]∀

U<sub>N−

1</sub>, or a derivative thereof, into a combined set, denoted [cP_N]∀

I_N, of user profiles for a set of users I_N=U_N ∩

U<sub>N−

1</sub>;

orthogonally transform [cP_N]∀

I_N into a set of abstract user profiles, denoted [aP_N] ∀

I_N, that are minimized in attribute classification dimension; and

reduce, in the attribute classification dimension, the abstract set [aP_N]]∀

I_N to an abstract reduced set, denoted [arP_N] ∀

I_N, of the user attributes having the highest variance, to enable enhanced personalized service recommendation to a user comprised in the common set I_N of users.

2. The system according to claim 1 further adapted and configured to:

calculate a transformation function, denoted [T_N], between the abstract reduced set [arP_N]∀

I_N and the combined user profile set [cP_N]∀

I_N; and

use the transformation function [T_N], or a derivative thereof, to calculate a set of abstract reduced user profiles [arP_N] pertaining to users comprised in a relative complement to the common set of users I_N, to enable the service provider S_N to create an enhanced service recommendation R_N to a user comprised in the relative complement to the common set of users I_N.

3. The system according to claim 2 further adapted and configured to:

store the transformation function [T_N] ∀

U_N∪

U<sub>N−

1</sub> or a derivative thereof in a service transformation database to enable later retrieval.

4. The system according to claim 2 further adapted and configured to store [arP_N] ∀

U_N∪

U<sub>N−

1</sub> in an abstract user profile database to enable later retrieval.

5. The system according to claim 2 further adapted and configured to:

operate in an iterative manner for each received new service specific set of user profiles [P_N] based on the derivatives [arP_N] and T_N, from previous iterations to enable system learning.

6. The system according to claim 1, further adapted and configured to transform orthogonally through Singular Value Decomposition factorization.

7. The system according to claim 1 further adapted and configured to reduce through principal component analysis.

8. The system according to claim 1 adapted and configured to reduce through obtaining a best rank r approximation.

9. The system according to claim 1, further comprising a memory unit adapted and configured to interact with the processing unit, and within which the abstract user profile database and the service transformation database are implemented.

10. A method for enabling, facilitating and accuracy enhancing personalized service recommendations via a network to a user using a new service, denoted S_N, comprising the steps of:

receiving, via an input/output network interface, a service specific set of user profiles, denoted [P_N], of user attributes classifying individual consumption history of a set of service users, denoted U_N, of the new service S_N;

combining, within a processing unit, the service specific set of user profiles, denoted [P_N], of user attributes and a set of previously received set of user attributes, denoted [P<sub>N−

1</sub>], or a derivative thereof, classifying a common set of users, denoted I_N, into a combined set of user profiles, denoted [P_N;

P<sub>N−

1</sub>], for the set of common users I_N;

transforming orthogonally, within the processing unit, the combined set of user profiles [P_N;

P<sub>N−

1</sub>] into a set [aP_N] of abstract user profiles that are minimized in dimension; and

reducing, within the processing unit, the abstract set [aP_N] to an abstract reduced set, denoted [arP_N], of the user attributes having the highest variance, to enable enhanced personalized service recommendation to a user comprised in the common set I_N of users.

11. The method according to claim 10 comprising the further steps performed by the processing unit:

calculating a transformation function, denoted [T_N], between the abstract reduced set [arP_N] and the combined user profile set [P_N;

P<sub>N−

1</sub>], such that [P_N] for I_N equals [T_N]·

[arP_N]; and

using the inverse, denoted [T<sup>−

1</sup>_N], of [T_N] to calculate a set of abstract reduced user profiles [arP_N] pertaining to users comprised in the relative complement to the common set of users I_N, thereby enabling the service provider S_N to create an enhanced service recommendation R_N to a user comprised in the relative complement to the common set of users I_N.

12. The method according to claim 11 comprising the further step of storing [T_N], [T⁻

1_N], or a derivative thereof, in a service transformation database to enable later retrieval.

13. The method according to claim 11 comprising the further step of storing [arP_N] in an abstract user profile database to enable later retrieval.

14. The method according to claim 11, comprising the further steps of iteratively repeating the method for each received new service specific set of user profiles [P_N] based on the derivatives [arP_N] and T_N, from previous iterations to enable system learning.

15. The method according to claim 10, wherein at least one transforming step comprises Singular Value Decomposition factorization.

16. The method according to claim 10, wherein at least one reducing step comprises principal component analysis.

17. The method according to claim 10, wherein at least one reducing step comprises obtaining a best rank r approximation.

18. A computer program comprising program code that is configured to perform the steps of claim 10 when executed by a computer.

19. A computer program product comprising program code stored on a computer readable medium and configured to perform the method of claim 10 when executed by a computer.