COMMUNITY RATING AND RANKING IN ENTERPRISE APPLICATIONS

• US 20110302102A1
• Filed: 06/03/2010
• Published: 12/08/2011
• Est. Priority Date: 06/03/2010
• Status: Abandoned Application

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: Yeleshwarapu, Chandra, Rangarajan, Keshava, Agarwal, Sudeep, Bismpigiannis, Athanasios, Srinivasan, Nagaraj, Rao, Aditya Ramamurthy, Rajesh, Narni, Ghosh, Bhaskar Jyoti
• Application Number:
  US12/793,375
• Publication Number:
  US 20110302102A1
• Time in Patent Office: Days
• US Class Current: 705/347
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• 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

The present invention is directed to methods and systems which provide a comprehensive rating and ranking of products and services. Furthermore, aspects of the present invention provides a complete review of products and services, as well as rankings of semantic and non-semantic reviews, which provides a “true” reflection of a product and/or service. As such, a calculation of a product/supplier rating based on all of its social entity contexts, is performed. This takes into account factors like, author (of social entity context) credibility, non-semantic (direct) rating, semantic rating calculated from the textual content of the social entity context, the community based credibility of the social entity context, and the like. Then, the community based credibility of a given social entity context is in turn calculated.

Claims

1. A computer system, comprising:

one or more processors; and

a storage device in communication with the one or more processors, wherein a rating and ranking system implemented by a rating and ranking application which is stored on the storage device, comprising a storage medium having a set of instructions stored thereon, executable by the one or more processors to perform the following operations;

identify a social entity context from a plurality of social entity contexts about a product;

determine a type of the social entity context;

based on the type of the social entity context, assign a weighted value to the first social entity context;

extract text from the social entity context;

analyze the extracted text from the social entity context to determine a semantic rating for the social entity context;

determine the social entity context'"'"'s author;

analyze the author to determine an author credibility rating for the author;

determine a non-semantic rating of the social entity context;

analyze one or more reviewers of the social entity context;

based on the semantic rating, author credibility rating, non-semantic rating, the one or more reviewers credibility rating, and the assigned weight, determine an overall rating of the social entity context;

based on an average of the overall rating of the social entity context and the plurality of social entity contexts, determine a social rating for the product;

determine an enterprise rating of the product; and

average the enterprise rating and the social rating of the product and generate a total rating for the product.

2. The computer system of claim 1, the rating and ranking application further comprises sets of instructions which, when executed by the one or more processors, cause the one or more processors to perform the operation of calculating a community based credibility of the social entity context based on the comments received.

3. The computer system of claim 2, wherein community based credibility of the social entity context comprises credibility arrived from community opinion.

4. The computer system of claim 1, wherein the weights are initially seeded.

5. The computer system of claim 4, wherein after the seeding of the weights, the weights automatically evolve over time based on data retrieved.

6. The computer system of claim 4, wherein the initial seeding is initiated by a system administrator.

7. The computer system of claim 1, wherein the social entity context comprises one or more of the following:

a blog post, a recommendation, an article, a review, a thread post, a forum post, a mail message, and an instant message.

8. The computer system of claim 1, wherein the semantic rating comprises a rating which has inferred relevance.

9. The computer system of claim 1, wherein the non-semantic rating comprises a rating which has a direct rating.

10. The computer system of claim 1, further comprising a weight computation engine coupled with the rating and ranking system, the weight computation engine configured to compute the weighted values.

11. A computer-readable medium having sets of instructions stored thereon which, when executed by a computer, cause the computer to:

identify a social entity context from a plurality of social entity contexts about a product;

determine a type of the social entity context;

based on the type of the social entity context, assign a weighted value to a first social entity context;

extract text from the social entity context;

analyze the extracted text from the social entity context to determine a semantic rating for the social entity context;

determine the social entity context'"'"'s author;

analyze the author to determine an author credibility rating for the author;

determine a non-semantic rating of the social entity context;

analyze one or more reviewers of the social entity context;

based on the semantic rating, author credibility rating, non-semantic rating, the one or more reviewers credibility rating, and the assigned weight, determine an overall rating of the social entity context;

based on an average of the overall rating of the social entity context and the plurality of social entity contexts, determine a social rating for the product;

determine an enterprise rating of the product; and

average the enterprise rating and the social rating of the product and generate a total rating for the product.

12. A method of implementing an rating and ranking application, the method comprising:

identifying a social entity context from a plurality of social entity contexts about a product;

determining a type of the social entity context;

based on the type of the social entity context, assigning a weighted value to a first social entity context;

extracting text from the social entity context;

analyzing the extracted text from the social entity context to determine a semantic rating for the social entity context;

determining the social entity context'"'"'s author;

analyzing the author to determine an author credibility rating for the author;

determining a non-semantic rating of the social entity context;

analyzing one or more reviewers of the social entity context;

based on the semantic rating, author credibility rating, non-semantic rating, the one or more reviewers credibility rating, and the assigned weight, determining an overall rating of the social entity context;

based on an average of the overall rating of the social entity context and the plurality of social entity contexts, determining a social rating for the product;

determining an enterprise rating of the product; and

averaging the enterprise rating and the social rating of the product and generating a total rating for the product.

13. The method of claim 12, further comprising calculating a community based credibility of the social entity context based on the comments received.

14. The method of claim 13, wherein community based credibility of the social entity context comprises credibility arrived from community opinion.

15. The method of claim 12, wherein the weights are initially seeded.

16. The method of claim 15, wherein after the seeding of the weights, the weights automatically evolve over time based on data retrieved.

17. The method of claim 15, wherein the initial seeding is initiated by a system administrator.

18. The method of claim 12, further comprising computing the weighted values.

19. The method of claim 12, wherein the semantic rating comprises a rating which has inferred relevance.

20. The method of claim 12, wherein the non-semantic rating comprises a rating which has a direct rating.