APPARATUS AND METHOD FOR MEASURING CONTENTS SIMILARITY BASED ON FEEDBACK INFORMATION OF RANKED USER AND COMPUTER READABLE RECORDING MEDIUM STORING PROGRAM THEREOF

• US 20110252044A1
• Filed: 04/12/2011
• Published: 10/13/2011
• Est. Priority Date: 04/13/2010
• Status: Active Grant

• Current Assignee: Konkuk University Industrial Cooperation Corp
• Original Assignee: Konkuk University Industrial Cooperation Corp
• Inventors: SHIN, Hyoseop
• Granted Patent: US 8,903,822 B2
• Time in Patent Office: Days
• US Class Current: 707/749
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536

Abstract

An apparatus for measuring contents similarity based on feedback information from ranked users includes: a user ranking unit for receiving from a database information on users as contents creators and feedback information on contents and ranking the users based on the received information; and a user grouping unit for classifying the users into a plurality of groups based on the ranking result. The apparatus further includes: a sequence modeling unit for receiving from the database the contents and the feedback information and modeling the number of feedbacks as sequences based on the ranked user groups; and a post similarity calculation unit for measuring similarity between the modeled sequences.

Claims

1. An apparatus for measuring contents similarity based on feedback information from ranked users, comprising:

a user ranking unit for receiving from a database information on users as contents creators and feedback information on posts as contents and ranking the users based on the received information;

a user grouping unit for classifying the users into a plurality of groups based on the ranking result;

a sequence modeling unit for receiving from the database the contents and the feedback information and modeling the number of feedbacks as sequences on the ranked user group basis; and

a post similarity calculation unit for measuring similarity between the modeled sequences to obtain similarity of the posts.

2. The apparatus of claim 1, wherein the user ranking unit calculates the user ranking based on a random walk-based algorithm or a HITS (Hyperlink-Induced Topic Search)-based algorithm.

3. The apparatus of a ranked user of claim 1, wherein the user grouping unit classifies the users into the groups by using an exponential classification method.

4. The apparatus of claim 1, wherein the sequence modeling unit models preference of the ranked user groups on each post based on an absolute quantity of feedbacks as in a following equation:

p=<

fb(p,G₁), fb(p,G_2), . . . , fb (p, G_N)>

,

wherein fb (p, G_k) indicates the number of feedbacks on a post p from users in a user group G_k.

5. The apparatus of claim 1, wherein the sequence modeling unit models preference of the user groups on each post based on a relative quantity of feedbacks as in a following equation:

6. The apparatus of claim 1, wherein the post similarity calculation unit calculates cosine similarity or an Euclidean distance while setting the sequences as vectors and performs sequence normalization using a maximum value and a minimum value as in a following equation:

7. The apparatus of claim 1, wherein the post similarity calculation unit calculates cosine similarity or an Euclidean distance while setting the sequences as vectors and performs sequence normalization using a preference threshold value as in a following equation:

8. The apparatus of claim 1, further comprising:

a similar post retrieval unit for retrieving a post similar to a random post by using similarity between the posts measured by the post similarity calculation unit;

a post clustering unit for clustering the posts based on the number of feedbacks on the posts and the similarity between the posts by using at least one of a majority selection clustering algorithm, a K-means clustering algorithm and an agglomerative hierarchical clustering (AHC) algorithm;

an

a post classification unit for classifying the posts based on preference of the ranked user groups.

9. A method for measuring contents similarity based on feedback information of ranked users, comprising:

receiving information on users as contents creators and feedback information on posts as contents from a database of a contents similarity measuring apparatus and ranking the users based on the received information;

classifying the users into a plurality of groups based on the ranking result;

receiving the contents and the feedback information received from the database and modeling the number of feedbacks as sequences on the ranked user group basis; and

measuring similarity between the modeled sequences to obtain similarity between the posts.

10. The method of claim 9, wherein in said ranking, the user ranking is calculated based on a random walk-based algorithm or a HITS (Hyperlink-Induced Topic Search)-based algorithm.

11. The method of claim 9, wherein in said classifying, the users are classified into the groups by using an exponential classification method.

12. The method of claim 9, wherein in said modeling, preference of the user groups on each post is modeled based on an absolute quantity of feedbacks as in a following equation:

p=<

fb(p,G₁), fb(p,G_2), . . . , fb(p, G_N)>

,

wherein fb(p, G_k) indicates the number of feedbacks on a post p from users in a user group G_k.

13. The method of claim 9, wherein in said modeling, preference of the user groups on each post is modeled based on a relative quantity of feedbacks as in a following equation:

14. The method of claim 9, wherein said measuring similarity, an Euclidean distance is calculated while setting the sequences as vectors, and sequence normalization using a maximum value and a minimum value is performed as in a following equation:

15. The method of claim 9, wherein in said measuring similarity, an Euclidean distance is calculated while setting the sequences as vectors, and sequence normalization using a preference threshold value is performed as in a following equation:

16. The method of claim 9, further comprising retrieving a post similar to a random post by using the similarity between the posts measured by a post similarity calculation unit.

17. The method of claim 9, further comprising clustering the posts based on the number of feedbacks and the similarity between the posts measured by a post similarity calculation unit by using at least one of a majority selection clustering algorithm, a K-means clustering algorithm, and an agglomerative hierarchical clustering (AHC) algorithm.

18. The method of claim 9, further comprising classifying the posts based on preference of the ranked user groups.

19. A computer readable recording medium encoded with a computer executable a program for performing the method described in claim 9.