Image Recommendation Algorithm Using Feature-Based Collaborative Filtering

Deok-Hwan KIM

doi:10.1587/transinf.E92.D.413

Image Recommendation Algorithm Using Feature-Based Collaborative Filtering

Deok-Hwan KIM

Full Text Views

0

Cite this

Summary :

As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.

Publication: IEICE TRANSACTIONS on Information Vol.E92-D No.3 pp.413-421

Publication Date: 2009/03/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E92.D.413

Type of Manuscript: PAPER

Category: Contents Technology and Web Information Systems

Cite this

Copy

Deok-Hwan KIM, "Image Recommendation Algorithm Using Feature-Based Collaborative Filtering" in IEICE TRANSACTIONS on Information, vol. E92-D, no. 3, pp. 413-421, March 2009, doi: 10.1587/transinf.E92.D.413.
Abstract: As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.413/_p

Copy

@ARTICLE{e92-d_3_413,
author={Deok-Hwan KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Image Recommendation Algorithm Using Feature-Based Collaborative Filtering},
year={2009},
volume={E92-D},
number={3},
pages={413-421},
abstract={As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.},
keywords={},
doi={10.1587/transinf.E92.D.413},
ISSN={1745-1361},
month={March},}

Copy

TY - JOUR
TI - Image Recommendation Algorithm Using Feature-Based Collaborative Filtering
T2 - IEICE TRANSACTIONS on Information
SP - 413
EP - 421
AU - Deok-Hwan KIM
PY - 2009
DO - 10.1587/transinf.E92.D.413
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2009
AB - As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.
ER -