DOI QR코드

DOI QR Code

A Study on Development of Hybrid Personalization Recommendation System Based on Learing Algorithm

학습알고리즘 기반의 하이브리드 개인화 추천시스템 개발에 관한 연구

  • 김용 (KT 마케팅연구소 스마트카드서비스개발실) ;
  • 문성빈 (연세대학교 문헌정보학과)
  • Published : 2005.09.01

Abstract

The popularization of the internet has produced an explosion in amount of the information. The importance of web personalization is being more and more increased. The personalization is realized by learning user's interest. User's interest is changing continuously and rapidly. We use user's profile to represent user's interest. User's profile is updated to reflect the change of user's interest. In this paper we present an adaptive learning algorithm that can be used to reflect user's interest that is changing with time. We propose the User's profile model. With this profile user's interest is learned based on user's feedback. This approach has applied to develop hybrid recommendation system.

Keywords

Personalization;Recommendation;Hybrid;Learning Algorithm;SDI

References

  1. 김현희, 구내영. 2002. 맟춤정보서비스를 위한 MyCyberLibrary 모형설계와 평가에 관한 연구. '정보관리학회지', 19(2): 132- 157
  2. 남궁 황. 2003. 학습시스템에 기반한 개인화 정보 서비스에 관한 연구. '정보관리학회지', 20(4): 112-134
  3. 황성희, 김영지, 이미희, 우용태. 2001. 인구통계학적 특성에 따른 협동적필터링 알고리즘의 추천 효율 분석. '한국데이타베이스 학회 춘계 논문집', 362-368
  4. Dahlen, B.J. and Konstan, J.A., Herlocker, J.L. 1998. Jump-starting movielens: User benefits of starting a colla- borative filtering system with 'dead data'. University of Minnesota TR 98-017
  5. Lang, K. 1995. 'Newsweeder: Learning to filter netnews.' Proceedings of the 12th International Conference on Machine Learning
  6. Manber, Udi, Ash Patel, and John Robison. 2000. 'Experience with personalization on yahoo!' Communication of the ACM, 43(8): 35-39 https://doi.org/10.1145/345124.345136
  7. Balabanovic, Marko, and Yoav Shoham. 1997. 'Content-Based Collaborative Recommendation.' Communications of the ACM, 40(3): 66-72 https://doi.org/10.1145/245108.245124
  8. Billsus, Daniel, and Michael J Pazzani. 1998. Learning Collaborative Information Filters. Berkeley: University of Cali- fornia Press
  9. Shardanand, Upendra, and Patti Maes. 1995. 'Social information filtering: Algorithms for automating 'Word of Mouth'. Proceedings of the ACM CHI '95 Conference on Human Fac- tors in Computing Systems, 210-217
  10. Sheth, Beerud Dilip. 1994. The learning approach to Personalized information Filtering. Boston: MIT Press.
  11. Wu, Yi-hung, Yong-chuan Chen, and Arbee l. P. Chen. 2001. 'Enabling Personalized recommendation on the Web based on User Interests and Behaviors.' Proceedings of Eleventh International Workshop on Data Engineering
  12. Schonberg, Edith, Thomas Cofino, Robert Hoch, Mark Podlaser, and Susan L. Spraragen. 2000. 'Measuring Success.' Communications of the ACM. 43(8): 53-57 https://doi.org/10.1145/345124.345142
  13. Krulwich, B., and Burkey, C.. 1996. 'Learning user information interests through extraction of semantically significant phrases.' Proceedings of the AAAI Spring Symposium on Machine Leaning in Information Access
  14. Resnick, Paul, Neophytos lacovou, Mitesh Suchak, et al. 1994. 'GroupLens: An Open Architecture for Collaborative Filtering of Netnews.' Proceedings of the Conference on Computer sup- ported Cooperative Work

Cited by

  1. Who Can be the Target of SNS Review Marketing? : A Study on the SNS Based Marketing Strategy vol.11, pp.3, 2012, https://doi.org/10.9716/KITS.2012.11.3.103