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http://dx.doi.org/10.3745/KIPSTD.2008.15-D.4.463

Image recommendation algorithm based on profile using user preference and visual descriptor  

Kim, Deok-Hwan (인하대학교 전자공학부)
Yang, Jun-Sik (인하대학교 대학원 전자공학과)
Cho, Won-Hee (인하대학교 대학원 전자공학과)
Publication Information
The KIPS Transactions:PartD / v.15D, no.4, 2008 , pp. 463-474 More about this Journal
Abstract
The advancement of information technology and the popularization of Internet has explosively increased the amount of multimedia contents. Therefore, the requirement of multimedia recommendation to satisfy a user's needs increases fastly. Up to now, CF is used to recommend general items and multimedia contents. However, general CF doesn't reflect visual characteristics of image contents so that it can't be adaptable to image recommendation. Besides, it has limitations in new item recommendation, the sparsity problem, and dynamic change of user preference. In this paper, we present new image recommendation method FBCF (Feature Based Collaborative Filtering) to resolve such problems. FBCF builds new user profile by clustering visual features in terms of user preference, and reflects user's current preference to recommendation by using preference feedback. Experimental result using real mobile images demonstrate that FBCF outperforms conventional CF by 400% in terms of recommendation ratio.
Keywords
Clustering; Image Segmentation; Recommendation System; Collaborative Filtering; Region-based Image Retrieval;
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