SIMILARITY CALCULATING METHOD AND APPARATUS

• US 20140136534A1
• Filed: 07/19/2013
• Published: 05/15/2014
• Est. Priority Date: 11/14/2012
• Status: Active Grant

• Current Assignee: Electronics and Telecommunications Research Institute
• Original Assignee: Electronics and Telecommunications Research Institute
• Inventors: LEE, Joo Young, UN, Sung Kyong, CHO, Hyun Sook, GIL, Youn Hee, KIM, Keon Woo, KIM, Young Soo, LEE, Sang Su, JO, Su Hyung, CHOI, Woo Yong
• Granted Patent: US 9,317,887 B2
• Time in Patent Office: Days
• US Class Current: 707/732
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A similarity calculating method and apparatus are disclosed. A similarity calculating method according to an exemplary embodiment of the present invention includes extracting similarity calculating data, which is determined in advance, by receiving a communication activity record for every user; modeling a communication activity pattern for every user and common information between the users based on the extracted similarity calculating data; and calculating a similarity between users using the modeled communication activity pattern for every user and common information. The modeling includes: modeling the communication activity pattern by calculating a value of a static feature from the similarity calculating data, and modeling the common information by calculating a value of a dynamic feature from the similarity calculating data.

Claims

1. A similarity calculating method, comprising:

extracting similarity calculating data, which is determined in advance, by receiving a communication activity record for every user;

modeling a communication activity pattern for every user and common information between the users based on the extracted similarity calculating data; and

calculating a similarity between users using the modeled communication activity pattern for every user and common information.

2. The similarity calculating method of claim 1, further comprising:

processing the extracted similarity calculating data to numerically represent at least a part of the data and build a relationship network for every user.

3. The similarity calculating method of claim 1, wherein the modeling includes:

modeling the communication activity pattern by calculating a value of a static feature from the similarity calculating data, and

modeling the common information by calculating a value of a dynamic feature from the similarity calculating data.

4. The similarity calculating method of claim 3, wherein the static feature includes whether to use a photograph, a moving image, or emoticon, a usage pattern based on the communication activity order, a transmitting/receiving time, a transmitting/receiving frequency, and the number of connections with the other user.

5. The similarity calculating method of claim 3, wherein the dynamic feature includes the number of commonly connected neighbors, whether to be directly or indirectly connected, whether to use the same keyword, whether to use the same pattern, whether to use the same object, and whether to use the same location.

6. The similarity calculating method of claim 3, wherein the calculating of a similarity includes:

calculating a static similarity by calculating a distance between elements of the static feature for every user,

calculating a dynamic similarity by applying a weight using the common information to each element of the dynamic feature for every user, and

calculating a similarity between the users using the calculated static similarity and dynamic similarity.

7. A computer readable recording media in which a program to execute the method of claim 1 is recorded.

14. A computer readable recording media in which a program to execute the method of claim 2 is recorded.

15. A computer readable recording media in which a program to execute the method of claim 3 is recorded.

16. A computer readable recording media in which a program to execute the method of claim 4 is recorded.

17. A computer readable recording media in which a program to execute the method of claim 5 is recorded.

18. A computer readable recording media in which a program to execute the method of claim 6 is recorded.

8. A similarity calculating apparatus, comprising:

a data extracting unit configured to extract similarity calculating data, which is determined in advance, by receiving a communication activity record for every user;

a modeling unit configured to model a communication activity pattern for every user and common information between the users based on the extracted similarity calculating data; and

a similarity calculating unit configured to calculate a similarity between users using the modeled communication activity pattern for every user and common information.

9. The similarity calculating apparatus of claim 8, further comprising:

a data converting unit configured to process the extracted similarity calculating data to numerically represent at least a part of the data and build a relationship network for every user.

10. The similarity calculating apparatus of claim 8, wherein the modeling unit includes:

a static feature modeling unit configured to model the communication activity pattern by calculating a value of a static feature from the similarity calculating data, and

a dynamic feature modeling unit configured to model the common information by calculating a value of a dynamic feature from the similarity calculating data.

11. The similarity calculating apparatus of claim 10, wherein the static feature includes whether to use of a photograph, a moving image, or emoticon, a usage pattern based on the communication activity order, a transmitting/receiving time, a transmitting/receiving frequency, and the number of connections with the other user.

12. The similarity calculating apparatus of claim 10, wherein the dynamic feature includes the number of commonly connected neighbors, whether to be directly or indirectly connected, whether to use the same keyword, whether to use the same pattern, whether to use the same object, and whether to use the same location.

13. The similarity calculating apparatus of claim 8, wherein the similarity calculating unit includes:

a static similarity calculating unit configured to calculate a static similarity by calculating a distance between elements of the static feature for every user;

a dynamic similarity calculating unit configured to calculate a dynamic similarity by applying a weight using the common information to each element of the dynamic feature for every user; and

a final similarity calculating unit configured to calculate a similarity between the users using the calculated static similarity and dynamic similarity.