Automatic Classification for Grouping Designs in Fashion Design Recommendation Agent System

  • Kyung-Yong Jung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)


Nowadays, users spend much time and effort in finding the best suitable designs since more and more information is placed on-line. To save their time and effort in searching the designs they want, the user-adapting recommendation system is required. In this paper, Automatic Classification for Grouping designs (ACG) in a Fashion Design Recommendation Agent System (FDRAS) is proposed. The ACG algorithm groups designs into clusters based on these classified designs. It is possible that if the design requires simultaneous regrouping in all other groups, the ACG algorithm can be used to improve efficiency of information retrieval and sorting, in the FDRAS datasets. The proposed method is evaluated on a large database, significantly outperforming the nearest-neighbor model and k-mean clustering in the prototype user-adapting FDRAS. This method can solve the large-scale dataset problem without deteriorating accuracy quality.


Collaborative Filter Average Similarity Automatic Classification Expert System Application Collaborative Filter Recommender System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kyung-Yong Jung
    • 1
  1. 1.School of Computer Information EngineeringSangji UniversityKorea

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