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Performance Evaluation of Personalized Textile Sensibility Design Recommendation System based on the Client-Server Model  

Jung Kyung-Yong (가천길대학 컴퓨터소프트웨어과)
Kim Jong-Hun (인하대학교 전자계산공학과)
Na Young-Joo (인하대학교 의류학과)
Lee Jung-Hyun (인하대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.11, no.2, 2005 , pp. 112-123 More about this Journal
Abstract
The latest E-commerce sites provide personalized services to maximize user satisfaction for Internet user The collaborative filtering is an algorithm for personalized item real-time recommendation. Various supplementary methods are provided for improving the accuracy of prediction and performance. It is important to consider these two things simultaneously to implement a useful recommendation system. However, established studies on collaborative filtering technique deal only with the matter of accuracy improvement and overlook the matter of performance. This study considers representative attribute-neighborhood, recommendation textile set, and similarity grouping that are expected to improve performance to the recommendation agent system. Ultimately, this paper suggests empirical applications to verify the adequacy and the validity on this system with the development of Fashion Design Recommendation Agent System (FDRAS ).
Keywords
Collaborative Filtering; Cognitive Engineering; User Interface;
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Times Cited By KSCI : 2  (Citation Analysis)
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