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Auto-tagging Method for Unlabeled Item Images with Hypernetworks for Article-related Item Recommender Systems  

Ha, Jung-Woo (서울대학교 전기컴퓨터공학부)
Kim, Byoung-Hee (서울대학교 전기컴퓨터공학부)
Lee, Ba-Do (서울대학교 전기컴퓨터공학부)
Zhang, Byoung-Tak (서울대학교 전기컴퓨터공학부)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.16, no.10, 2010 , pp. 1010-1014 More about this Journal
Abstract
Article-related product recommender system is an emerging e-commerce service which recommends items based on association in contexts between items and articles. Current services recommend based on the similarity between tags of articles and items, which is deficient not only due to the high cost in manual tagging but also low accuracies in recommendation. As a component of novel article-related item recommender system, we propose a new method for tagging item images based on pre-defined categories. We suggest a hypernetwork-based algorithm for learning association between images, which is represented by visual words, and categories of products. Learned hypernetwork are used to assign multiple tags to unlabeled item images. We show the ability of our method with a product set of real-world online shopping-mall including 1,251 product images with 10 categories. Experimental results not only show that the proposed method has competitive tagging performance compared with other classifiers but also present that the proposed multi-tagging method based on hypernetworks improves the accuracy of tagging.
Keywords
Hypernetwork; Image auto-tagging; Multi-label classification; Item recommender system;
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