Method to hierarchical pooling of opinions from multiple sources

• US 7,130,777 B2
• Filed: 11/26/2003
• Issued: 10/31/2006
• Est. Priority Date: 11/26/2003
• Status: Expired due to Fees

• Current Assignee: LinkedIn Corporation (Microsoft Corporation)
• Original Assignee: International Business Machines Corporation
• Inventors: Garg, Ashutosh, Zhu, Huaiyu, Vaithyanathan, Shivakumar, Thathachar, Jayram S.
• Primary Examiner(s): PALADINI, ALBERT WILLIAM
• Application Number:
  US10/723,471
• Publication Number:
  US 20050114161A1
• Time in Patent Office: 1,070 Days
• Field of Search: 703/2, 702/181, 706/45, 706/46, 706/52, 434/322
• US Class Current: 703/2
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products

Abstract

Disclosed is a system, method, and program storage device of aggregating opinions comprising consolidating a plurality of expressed opinions on various dimensions of topics as discrete probability distributions, generating an aggregate opinion as a single point probability distribution by minimizing a sum of weighted divergences between a plurality of the discrete probability distributions, and presenting the aggregate opinion as a Bayesian network, wherein the divergences comprise Kullback-Liebler distance divergences, and wherein the expressed opinions are generated by experts and comprise opinions on sentiments of products and services. Moreover, the aggregate opinion predicts success of the products and services. Furthermore, the experts are arranged in a hierarchy of knowledge, wherein the knowledge comprises the various dimensions of topics for which opinions may be expressed upon.

Claims

1. A method of aggregating opinions, said method comprising:

consolidating a plurality of expressed opinions on various dimensions of topics as discrete probability distributions; and

generating an aggregate opinion as a single point probability distribution by minimizing a sum of weighted divergences between a plurality of said discrete probability distributions.

2. The method of claim 1, wherein said divergences comprise Kullback-Liebler distance divergences.

3. The method of claim 1, wherein said expressed opinions comprise opinions on sentiments of products and services.

4. The method of claim 3, wherein said aggregate opinion predicts success of said products and services.

5. The method of claim 1, wherein said expressed opinions are generated by experts.

6. The method of claim 5, wherein said experts are arranged in a hierarchy of knowledge, wherein said knowledge comprises said various dimensions of topics for which opinions may be expressed upon.

7. The method of claim 1, further comprising presenting said aggregate opinion as a Bayesian network.

21. The system of claim 1, wherein said processor presents said aggregate opinion as a Bayesian network.

8. A method of pooling opinions, said method comprising:

representing a plurality of expressed opinions received from a plurality of sources on various dimensions of topics as discrete probability distributions;

creating a single point probability distribution by minimizing a sum of weighted divergences between a plurality of said discrete probability distributions; and

generating an aggregate opinion based on said single point probability distribution.

9. The method of claim 8, wherein said divergences comprise Kullback-Liebler distance divergences.

10. The method of claim 8, wherein said expressed opinions comprise opinions on sentiments of products and services.

11. The method of claim 10, wherein said aggregate opinion predicts success of said products and services.

12. The method of claim 8, wherein said expressed opinions are generated by experts.

13. The method of claim 12, wherein said experts are arranged in a hierarchy of knowledge, wherein said knowledge comprises said various dimensions of topics for which opinions may be expressed upon.

14. The method of claim 8, further comprising presenting said aggregate opinion as a Bayesian network.

15. A system for aggregating opinions comprising:

a network operable for consolidating a plurality of expressed opinions on various dimensions of topics as discrete probability distributions; and

a processor operable for generating an aggregate opinion as a single point probability distribution by minimizing a sum of weighted divergences between a plurality of said discrete probability distributions.

16. The system of claim 15, wherein said divergences comprise Kullback-Liebler distance divergences.

17. The system of claim 15, wherein said expressed opinions comprise opinions on sentiments of products and services.

18. The system of claim 17, wherein said aggregate opinion predicts success of said products and services.

19. The system of claim 15, wherein said expressed opinions are generated by experts.

20. The system of claim 19, wherein said experts are arranged in a hierarchy of knowledge, wherein said knowledge comprises said various dimensions of topics for which opinions may be expressed upon.

22. A system for aggregating opinions comprising:

means for consolidating a plurality of expressed opinions on various dimensions of topics as discrete probability distributions; and

means for generating an aggregate opinion as a single point probability distribution by minimizing a sum of weighted divergences between a plurality of said discrete probability distributions.

23. A program storage device readable by computer, tangibly embodying a program of instructions executable by said computer to perform a method of aggregating opinions, said method comprising:

consolidating a plurality of expressed opinions on various dimensions of topics as discrete probability distributions; and

generating an aggregate opinion as a single point probability distribution by minimizing a sum of weighted divergences between a plurality of said discrete probability distributions.

24. The program storage device of claim 23, wherein said divergences comprise Kullback-Liebler distance divergences.

25. The program storage device of claim 23, wherein said expressed opinions comprise opinions on sentiments of products and services.

26. The program storage device of claim 25, wherein said aggregate opinion predicts success of said products and services.

27. The program storage device of claim 23, wherein said expressed opinions are generated by experts.

28. The program storage device of claim 27, wherein said experts are arranged in a hierarchy of knowledge, wherein said knowledge comprises said various dimensions of topics for which opinions may be expressed upon.

29. The program storage device of claim 23, wherein said method further comprises presenting said aggregate opinion as a Bayesian network.