Similarity calculating method and apparatus

• US 9,317,887 B2
• Filed: 07/19/2013
• Issued: 04/19/2016
• 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
• Primary Examiner(s): Ly, Anh
• Application Number:
  US13/946,693
• Publication Number:
  US 20140136534A1
• Time in Patent Office: 1,005 Days
• Field of Search: 707/732, 707/737, 707/711, 707/741, 707/748, 707/749, 707/758, 707/724, 707/722, 707/728, 707/759, 707/769, 707/755, 707/803, 707/E17.051, 707/E17.009, 707/E17.014, 707/E17.108, 707/E17.069, 707/E17.01, 707/9, 707/E17.089, 707/E17.044, 707/E17.033, 707/E17.134, 705/7.12, 705/7.29, 705/10, 705/1.1, 705/14.53, 705/14.49, 705/14.66, 705/319, 705/342, 705/500, 709/203, 709/204, 709/205, 709/223, 709/224, 709/233, 715/752, 715/733, 382/190, 382/165, 382/125, 382/118, 382/117, 382/115, 382/106, 382/103, 382/107, 382/236, 725/116
• US Class Current: 1/1
• 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 computer implemented 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 by calculating a value of a static feature from the extracted similarity calculating data;

modeling common information between the users by calculating a value of a dynamic feature from the extracted similarity calculating data;

calculating a static similarity for each user by using elements of the static feature to which the modeled communication activity pattern is reflected;

calculating a dynamic similarity by using the modeled common information for each element of the dynamic feature for every user; and

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

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 static feature includes an average number of photographs, moving images, or emoticons included in a message, a usage pattern based on a communication activity order, a transmitting/receiving time, a transmitting/receiving frequency, and a number of connections with another user.

4. The similarity calculating method of claim 1, wherein the dynamic feature includes a number of commonly connected neighbors, a degree of connection, a common keyword, a common pattern, a common object, and a common location.

5. The similarity calculating method of claim 1, wherein

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

the dynamic similarity is calculated by applying a weight using the modeled common information to each element of the dynamic feature for every user.

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

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

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

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

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

7. 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 static feature modeling unit configured to model a communication activity pattern for every user by calculating a value of a static feature from the extracted similarity calculating data;

a dynamic feature modeling unit configured to model common information between the users by calculating a value of a dynamic feature from the extracted similarity calculating data; and

a similarity calculating unit configured to calculate a similarity between the users using the modeled communication activity pattern and the modeled common information, wherein the similarity calculating unit includes;

a static similarity calculating unit configured to calculate a static similarity for every user using elements of the static feature to which the modeled communication activity pattern is reflected,

a dynamic similarity calculating unit configured to calculate a dynamic similarity using the modeled common information for each element of the dynamic feature for every user, and

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

8. The similarity calculating apparatus of claim 7, 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.

9. The similarity calculating apparatus of claim 7, wherein the static feature includes an average number of photographs, moving images, or emoticons included in a message, a usage pattern based on a communication activity order, a transmitting/receiving time, a transmitting/receiving frequency, and a number of connections with another user.

10. The similarity calculating apparatus of claim 7, wherein the dynamic feature includes a number of commonly connected neighbors, a degree of connection, a common keyword, a common pattern, a common object, and a common location.

11. The similarity calculating apparatus of claim 7, wherein

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

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