Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Development of educational program for quick response system on textile and fashion e-business
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: interaction platforms and techniques
An effective recommendation algorithm for clustering-based recommender systems
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
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As fashion E-business is coming, it is becoming important to provide the analysis of preferences that is becoming increasingly more customer oriented. Consumers caused the diversification of the fashion product because they seek fashion and individuality in order to satisfy their needs. In this paper, we proposed the quick response system using the collaborative filtering on fashion E-business. The proposed method applies the developed quick response system to increase the efficiency of merchandising for the products of design styles. Collaborative filtering was adopted in order to recommend final design styles of interest for designers based on the predictive relationship discovered between the current designer and other previous designers. Ultimately, this paper suggests empirical applications to verify the adequacy and the validity of our system.