Image Recommendation Algorithm Using Feature-Based Collaborative Filtering

Deok-Hwan KIM  

IEICE TRANSACTIONS on Information and Systems   Vol.E92-D   No.3   pp.413-421
Publication Date: 2009/03/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E92.D.413
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Contents Technology and Web Information Systems
feature clustering,  image segmentation,  recommendation,  collaborative filtering,  region-based image retrieval,  

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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.