Advertisement

Discovery of User Preference in Personalized Design Recommender System through Combining Collaborative Filtering and Content Based Filtering

  • Kyung-Yong Jung
  • Jason J. Jung
  • Jung-Hyun Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2843)

Abstract

More and more recommender systems build close relationships with their users by adapting to their needs and therefore providing a personal experience. One aspect of personalization is the recommendation and presentation of information and products so that users can access the recommender system more efficiently. However, powerful filtering technology is required in order to identify relevant items for each user. In this paper we describe how collaborative filtering and content-based filtering can be combined to provide better performance for information filtering. We propose the personalized design recommender system of textile design applying both technologies as one of the methods in the material development centered on customer’s sensibility and preference. Finally, we plan to conduct empirical applications to verify the adequacy and the validity of our personalized design recommender system.

Keywords

User Preference Color Histogram Textile Design Collaborative Filter Mean Absolute Error 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Balabanovic, M., Shoham, Y.: Fab: Content-based, Collaborative Recommendation. Communication of the Association of Computing Machinery 40(3), 66–72 (1997)Google Scholar
  2. 2.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proc. of the 14th Conference on Uncertainty in AI (1998)Google Scholar
  3. 3.
    Herlocker, J., et al.: An Algorithm Framework for Performing Collaborative Filtering. In: Proc. of ACM SIGIR 1999 (1999)Google Scholar
  4. 4.
    Sarwar, B.M., et al.: Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system. In: Proc. of ACM CSCW 1998 (1998)Google Scholar
  5. 5.
    Jung, K.Y., Ryu, J.K., Lee, J.H.: A New Collaborative Filtering Method using Representative Attributes-Neighborhood and Bayesian Estimated Value. In: Proc. of ICAI 2002, USA, pp. 709–715 (2002)Google Scholar
  6. 6.
    Jung, K.Y., Lee, J.H.: Prediction of User Preference in Recommendation System using Association User Clustering and Bayesian Estimated Value. In: McKay, B., Slaney, J.K. (eds.) Canadian AI 2002. LNCS (LNAI), vol. 2557, pp. 284–296. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Pazzani, M.: A Framework for Collaborative, Content-Based and Demographic Filtering. AI Review, 393–408 (1999)Google Scholar
  8. 8.
    Resnick, P., et al.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proc. of ACM CSCW 1994, pp. 175–186 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Kyung-Yong Jung
    • 1
  • Jason J. Jung
    • 1
  • Jung-Hyun Lee
    • 1
  1. 1.Department of Computer Science & EngineeringInha UniversityInchonKorea

Personalised recommendations