METHOD AND APPARATUS FOR RECOMMENDING IMAGE BASED ON USER PROFILE USING FEATURE-BASED COLLABORATIVE FILTERING TO RESOLVE NEW ITEM RECOMMENDATION

• US 20100088151A1
• Filed: 02/20/2009
• Published: 04/08/2010
• Est. Priority Date: 10/08/2008
• Status: Abandoned Application

• Current Assignee: INHA Industry Partnership Institute
• Original Assignee: INHA Industry Partnership Institute
• Inventors: YANG, Jun Sik, CHO, Won Hee, KIM, Deok Hwan
• Application Number:
  US12/390,361
• Publication Number:
  US 20100088151A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06F 16/50 of still image data
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

Provided are a method and apparatus for recommending an image based on a user profile using feature-based collaborative filtering. To generate the user profile, a model may be build from a customer purchase list database for each predetermined time. A multimedia image may be recommended, in which a purchase likeness score of a target user is high or at a predetermined level, by using the built model.

Claims

1. A method for recommending a multimedia image based on a user profile using feature-based collaborative filtering, the method comprising:

building a model from a customer purchase list database for each predetermined time to generate the user profile; and

recommending the multimedia image, in which a purchase likeness score of a target user is at a predetermined level, by using the built model.

2. The method of claim 1, wherein the generating of the user profile comprises:

dividing an image into a plurality of meaning regions on all images of the customer purchase list database by using a feature vector of a multidimensional attribute space;

extracting a feature from the divided regions of the image to map the extracted feature on a feature space; and

analyzing the customer purchase list database, representing an image purchased by a user as a set of feature clusters based on a user'"'"'s rating, and generating the user profile.

3. The method of claim 2, wherein the dividing of the image comprises treating each pixel of all the images of the customer purchase list database as a dot of the feature space by using the feature vector of the multidimensional attribute space, and dividing the image by bunching similar pixels according to a selected feature.

4. The method of claim 2, wherein the feature extracted from the divided regions of the image comprises at least one of a size of the region, a position of the region, a second moment, a color of the region, and texture, which are extracted from the divided regions of the image.

5. The method of claim 2, wherein the feature cluster comprises at least one of dots represented as regions of a plurality of images that an arbitrary user purchased, a center, variance and effective radius of a cluster and information for a user that has purchased the image of the cluster.

6. The method of claim 1, wherein the recommending of the multimedia image comprises:

setting a neighborhood by using multimedia image contents in profiles of a target user and an arbitrary user; and

generating an image recommendation list on the basis of the set neighborhood.

7. The method of claim 6, wherein the setting of the neighborhood comprises:

configuring each cluster by using the multimedia image contents in the profiles of the target user and the arbitrary user;

calculating a distance between the each cluster through a query;

selecting a neighbor cluster according to the calculated distance; and

setting a similarity cluster for a target user of the neighbor cluster.

8. The method of claim 7, further comprising:

determining a cluster to enter a new multimedia image content, when the new multimedia image content, which was not purchased in the past and is not comprised in a cluster of each user, is provided; and

entering the new multimedia image content into the similarity cluster, when the new multimedia image content is within an effective radius of the determined cluster.

9. The method of claim 6, wherein the generating of the image recommendation list comprises extracting the specific number of upper multimedia image contents, in which a frequency of purchase is high, from the set neighborhood to generate the image recommendation list.

10. A computer-readable storage medium storing a program to recommend a multimedia image based on a user profile using feature-based collaborative filtering, comprising instructions to cause a computer or an apparatus to:

build a model from a customer purchase list database for each predetermined time to generate the user profile; and

recommend the multimedia image, in which a purchase likeness score of a target user is at a predetermined level, by using the built model.

11. The computer-readable storage medium of claim 10, wherein to generate the user profile, further comprising instructions to cause the computer or the apparatus to:

divide an image into a plurality of meaning regions on all images of the customer purchase list database by using a feature vector of a multidimensional attribute space;

extract a feature from the divided regions of the image to map the extracted feature on a feature space; and

analyze the customer purchase list database, represent an image purchased by a user as a set of feature clusters based on a user'"'"'s rating, and generate the user profile.

12. The computer-readable storage medium of claim 11, wherein to divide the image, further comprising instructions to cause the computer or the apparatus to:

treat each pixel of all the images of the customer purchase list database as a dot of the feature space by using the feature vector of the multidimensional attribute space; and

divide the image by bunching similar pixels according to a selected feature.

13. The computer-readable storage medium of claim 11, wherein the feature extracted from the divided regions of the image comprises at least one of a size of the region, a position of the region, a second moment, a color of the region, and texture, which are extracted from the divided regions of the image.

14. The computer-readable storage medium of claim 11, wherein the feature cluster comprises at least one of dots represented as regions of a plurality of images that an arbitrary user purchased, a center, variance and effective radius of a cluster and information for a user that has purchased the image of the cluster.

15. The computer-readable storage medium of claim 10, wherein to recommend the multimedia image, further comprising instructions to cause the computer or the apparatus to:

set a neighborhood by using multimedia image contents in profiles of a target user and an arbitrary user; and

generate an image recommendation list on the basis of the set neighborhood.

16. The computer-readable storage medium of claim 15, wherein to set the neighborhood, further comprising instructions to cause the computer or the apparatus to:

configure each cluster by using the multimedia image contents in the profiles of the target user and the arbitrary user;

calculate a distance between the each cluster through a query;

select a neighbor cluster according to the calculated distance; and

set a similarity cluster for a target user of the neighbor cluster.

17. The computer-readable storage medium of claim 16, further comprising instructions to cause the computer or the apparatus to:

determine a cluster to enter a new multimedia image content, when the new multimedia image content, which was not purchased in the past and is not comprised in a cluster of each user, is provided; and

enter the new multimedia image content into the similarity cluster, when the new multimedia image content is within an effective radius of the determined cluster.

18. The computer-readable storage medium of claim 15, wherein to generate the image recommendation list, further comprising instructions to cause the computer or the apparatus to extracting the specific number of upper multimedia image contents, in which a frequency of purchase is high, from the set neighborhood to generate the image recommendation list.

19. An apparatus for recommending a multimedia image based on a user profile using feature-based collaborative filtering, the apparatus comprising:

an image dividing unit to divide an image into a plurality of meaning regions on all images of a customer purchase list database by using a feature vector;

a feature extracting unit to extract a feature from the regions of the image divided by the image dividing unit to map the extracted feature on a feature space;

a user profile generating unit to analyze the customer purchase list database, represent an background image purchased by a user as a set of feature clusters based on a user'"'"'s rating, and generate the user profile;

a neighborhood setting unit to set a neighborhood by using multimedia image contents in profiles of a target user and an arbitrary user; and

a recommendation list generating unit to generate a background image recommendation list on the basis of the neighborhood set by the neighborhood setting unit.