Systems and methods for measuring behavior characteristics

• US 8,055,663 B2
• Filed: 12/20/2006
• Issued: 11/08/2011
• Est. Priority Date: 10/30/2006
• Status: Active Grant

• Current Assignee: Institute For Information Industry
• Original Assignee: Institute For Information Industry
• Inventors: Tsai, Tse-Ming, Shih, Chia-Chun
• Primary Examiner(s): Gishnock; Nikolai A
• Application Number:
  US11/641,705
• Publication Number:
  US 20080103721A1
• Time in Patent Office: 1,784 Days
• Field of Search: 434/236, 707/5, 707/7, 707/102, 707/513, 707/514, 707/531, 707/734, 707748-751
• US Class Current: 707/749
• CPC Class Codes: G06Q 10/00 Administration; Management

Abstract

Systems and methods for measuring behavior characteristics. For at least one specific user, a first concern score for respective key terms is calculated according to use frequency of respective key terms of network content corresponding to the specific user and all users. A first relation matrix for at least one specific key term is calculated according to at least two users corresponding to respective interaction behaviors between the key terms and a type weighting corresponding to respective interaction behaviors. A first interaction score for the specific user regarding the specific key term is calculated according to the first relation matrix. A first characteristic score for the specific user regarding the specific key term is calculated according to the first concern score and the first interaction score.

Claims

1. A system for measuring behavior characteristics stored in a non-transitory machine-readable storage medium, comprising:

a domain hierarchy comprising a plurality of concepts and associations therebetween;

a database storing network content for a plurality of users, where the network content comprises a plurality of key terms, and a plurality of interaction behaviors therebetween, wherein each of the interaction behaviors involves at least two users, and has a corresponding type weighting, in which different interaction behaviors have different type weightings;

a term-concept association matrix recording association degrees for respective key terms toward respective concepts; and

a processing module, for at least one specific user, calculating a concern score for respective concepts according to use frequency of respective key terms corresponding to the specific user, use frequency of respective key terms corresponding to all users, and the term-concept association matrix, calculating a relation matrix for at least one specific concept according to the at least two users corresponding to respective interaction behaviors, the type weighting corresponding to respective interaction behaviors, and the association degree for at least one key term used in the interaction behaviors toward the specific concept, calculating an interaction score for the specific user regarding the specific concept according to the relation matrix using an algorithm, and calculating a characteristic score for the specific user regarding the specific concept according to the concern score and the interaction score,

wherein the processing module calculates the relation matrix according to the formula, wherein “

x”

in the formula means a cross product;

2. The system of claim 1 wherein the network content comprises a plurality of network articles.

3. The system of claim 2 wherein the key terms comprise at least a tag or category of respective network articles.

4. The system of claim 2 wherein the interaction behaviors comprise comments, trackbacks, links, subscriptions, recommendations or blogrolls.

5. The system of claim 1 wherein the processing module calculates the concern score according to the formula, wherein “

x”

in the formula means a cross product;

6. The system of claim 1 wherein the algorithm comprises a HITS (Hypertext-Induced Topic Search) algorithm.

7. The system of claim 6 wherein the processing module calculates the characteristic score according to the formula, wherein “

x”

in the formula means a cross product;

B_U=G_U+k×

IA_U,

wherein B_U represents the characteristic score, G_U represents the concern score, IA_U represents the interaction score, and k is a participation weighting for the interaction behaviors.

8. The system of claim 7 wherein the interaction score comprises a hub score or an authority score.

9. A computer-implemented method for measuring behavior characteristics for use in a computer, wherein the computer is programmed to perform the steps of:

providing a domain hierarchy comprising a plurality of concepts and associations therebetween;

providing network content for a plurality of users, where the network content comprises a plurality of key terms, and a plurality of interaction behaviors therebetween, wherein each of the interaction behaviors involves at least two users, and has a corresponding type weighting, in which different interaction behaviors have different type weightings;

providing a term-concept association matrix recording association degrees for respective key terms toward respective concepts;

for at least one specific user, calculating a concern score for respective concepts according to use frequency of respective key terms corresponding to the specific user, use frequency of respective key terms corresponding to all users, and the term-concept association matrix;

calculating a relation matrix for at least one specific concept according to the at least two users corresponding to respective interaction behaviors, the type weighting corresponding to respective interaction behaviors, and the association degree for at least one key term used in the interaction behaviors toward the specific concept;

calculating an interaction score for the specific user regarding the specific concept according to the relation matrix using an algorithm; and

calculating a characteristic score for the specific user regarding the specific concept according to the concern score and the interaction score,

wherein the relation matrix is calculated according to the formula, wherein “

x”

in the formula means a cross product;

10. The method of claim 9 wherein the network content comprises a plurality of network articles.

11. The method of claim 10 wherein the key terms comprise at least a tag or category of respective network articles.

12. The method of claim 10 wherein the interaction behaviors comprise comments, trackbacks, links, subscriptions, recommendations or blogrolls.

13. The method of claim 9 further comprising calculating the concern score according to the formula, wherein “

x”

in the formula means a cross product;

14. The method of claim 9 wherein the algorithm comprises a HITS (Hypertext-Induced Topic Search) algorithm.

15. The method of claim 14 further comprising calculating the characteristic score according to the formula, wherein “

x”

in the formula means a cross product;

B_U=G_U+k×

IA_U,

wherein B_U represents the characteristic score, G_U represents the concern score, IA_U represents the interaction score, and k is a participation weighting for the interaction behaviors.

16. The method of claim 15 wherein the interaction score comprises a hub score or an authority score.

17. A non-transitory machine-readable storage medium comprising a computer program, which, when executed, causes a device to perform a method for measuring behavior characteristics, the method comprising:

providing a domain hierarchy comprising a plurality of concepts and associations therebetween;

providing network content for a plurality of users, where the network content comprises a plurality of key terms, and a plurality of interaction behaviors therebetween, wherein each of the interaction behaviors involves at least two users, and has a corresponding type weighting, in which different interaction behaviors have different type weightings;

providing a term-concept association matrix recording association degrees for respective key terms toward respective concepts;

for at least one specific user, calculating a concern score for respective concepts according to use frequency of respective key terms corresponding to the specific user, use frequency of respective key terms corresponding to all users, and the term-concept association matrix;

calculating a relation matrix for at least one specific concept according to the at least two users corresponding to respective interaction behaviors, the type weighting corresponding to respective interaction behaviors, and the association degree for at least one key term used in the interaction behaviors toward the specific concept;

calculating an interaction score for the specific user regarding the specific concept according to the relation matrix using an algorithm; and

calculating a characteristic score for the specific user regarding the specific concept according to the concern score and the interaction score,

wherein the relation matrix is calculated according to the formula, wherein “

x”

in the formula means a cross product;