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Design and Implementation of a Contents Recommendation System in Mobile Environments

모바일 환경에서 콘텐츠 추천 시스템 설계 및 구현

  • Received : 2011.09.20
  • Accepted : 2011.10.05
  • Published : 2011.12.28

Abstract

The key issues of recommendation systems provide the contents satisfying the interests of users for the huge amounts of contents over internet. The existing recommendation system use the algorithms considering the users' profiles and context information to enhance the exactness of a recommendation. However, the existing recommendation system can't satisfy the requirements of service providers because the business models of service providers is not considered. In this paper, we propose the mobile recommendation system using the composite contexts and the recommendation weights applying the business model of service providers. The proposed system retrieves the contents of the contents providers using composite context information and apply the recommendation weights to recommend the suitable contents for the business models of service providers. Therefore, we provide the contents satisfying the consumption value of users and the business models of service providers to mobile users.

Keywords

Mobile Contents;Context information;Composite Context;Recommendation System

Acknowledgement

Supported by : 한국연구재단

References

  1. 홍성태, 임일, "웹 2.0 환경에서 정보 분류와 필터링, 그리고 협업을 위한 기술의 동향 및 발전 방향", Telecommunications Review, 제17권, 제4호, pp.643-650, 2007.
  2. G. Adomavicius and A. Tuzhilin, "Toward the Next Generation of Recommender Systems : A Survey of the State-of-the-Art and Possible Extensions," IEEE Transactions on Knowledge and Data Engineering, Vol.17, No.6, pp.734-749, 2005. https://doi.org/10.1109/TKDE.2005.99
  3. Q. Li, S. H. Myaeng, D. Guan, and B. M. Kim, "A Probabilistic Model for Music Recommendation Considering Audio Features," Proc. Asia Information Retrieval Symposium, pp.72-83, 2005.
  4. D. Billsus and M. J. Pazzani, "Learning Collaborative Information Filters," Proc. International Conference on Machine Learning, pp.46-53, 1998.
  5. X. Su and T. M. Khoshgoftaar, "A Survey of Collaborative Filtering Techniques," Advances in Artificial Intelligence, Vol.2009, pp.1-19, 2009.
  6. 이세일, 이상용, "상대적 분류 방법과 시간에 따른 평가값 보정을 적용한 협력적 필터링 기반 추천 시스템", 한국지능시스템학회논문지, 제20권, 제2 호, pp.189-194, 2010. https://doi.org/10.5391/JKIIS.2010.20.2.189
  7. M. Balabanovic and Y. Shoham, "Fab : content-based, collaborative recommendation," Communications of the ACM, Vol.40, No.3, pp.66-72, 1997. https://doi.org/10.1145/245108.245124
  8. 강용진, 선철용, 박규식, "복합 필터링을 이용한 IPTV-VOD 프로그램 추천 시스템 연구", 전자공 학회논문지-SP, 제47권 SP편, 제4호, pp.9-19, 2010.
  9. W. Worndl, C. Schüller, and R. Wojtech, "A Hybrid Recommender System for Context-aware Recommendations of Mobile Applications," Proc. International Conference on Data Engineering Workshops, pp.871-87, 2007.
  10. D. M. Shin, J. W. Lee, J. H. Yeon, and S. G. Lee, "Context-Aware Recommendation by Aggregating User Context," Proc. IEEE Conference on Commerce and Enterprise Computing, pp.423-430, 2009.
  11. Z. Yujie and W. Licai, "Some Challenges for Context-aware Recommender Systems," Proc. International Conference on Computer Science & Education, pp.362-365, 2010.
  12. J. H. Su, H. H. Yeh, P. S. Yu, and V. S. Tseng, "Music Recommendation Using Content and Context Information Mining," IEEE Intelligent Systems(EXPERT), Vol.25, No.1, pp.16-26, 2010.
  13. 이세일, 이상용, "컨텍스트 기반 협력적 필터링을 이용한 추천 시스템", 한국지능시스템학회 논문지, 제21권, 제2호, pp.224-229, 2011. https://doi.org/10.5391/JKIIS.2011.21.2.224
  14. 이동주, 이상근, 이상구, "시간 상황 정보를 고려한 협업 필터링을 이용한 음악 추천", 한국정보과학회 한국컴퓨터종합학술대회 논문집, 제36권, 제 1호(C), pp.123-28, 2009.
  15. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, "GroupLens : An Open Architecture for Collaborative Filtering of Netnews," Proc. ACM Conference on Computer Supported Cooperative Work, pp.175-186, 1994.
  16. 연철, 지애띠, 김흥남, 조근식, "효과적인 추천 시스템을 위한 협업적 태그 기반의 여과 기법", 지능정보연구, 제14권, 제2호, pp.157-177, 2008.
  17. 신택수, 장근녕, 박유진, "선호도 추정모형과 협업 필터링기법을 이용한 고객추천시스템", 한국지능정보시스템학회논문지, 제12권, 제4호, pp.1-14, 2006.
  18. 김병오, 한동숭, "상황인식 및 음원 속성에 따른 공간 설치형 음악 추천 시스템, DJ로봇", 한국콘텐츠학회 논문지, 제10권, 제6호, pp.286-297, 2010. https://doi.org/10.5392/JKCA.2010.10.6.286
  19. 김귀정, 김봉한, 한정수, "복합지식 기반 개인 맞춤형 지능화 추천 시스템", 한국콘텐츠학회논문지, 제10권, 제8호, pp.26-31, 2010.

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