지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국데이터전략학회 Journal of Information Technology Applications & Management Journal of Information Technology Applications & Management Vol.23 No.1
- 발행연도
- 2016.3
- 수록면
- 33 - 43 (11page)
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초록· 키워드
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Along with the spread of digital music and recent growth in the digital music industry, the demands for music recommender are increasing. These days, listeners have increasingly preferred to digital real-time streamlining and downloading to listen to music because it is convenient and affordable for the listeners to do that. We use Bayesian learning through weight of listener’s prefered music site such as Melon, Billboard, Bugs Music, Soribada, and Gini. We reflect most popular current songs across all genres and styles for music recommender system using user profile. It is necessary for us to make the task of preprocessing of clustering the preference with weight of listener’s preferred music site with popular music charts. We evaluated the proposed system on the data set of music sites to measure its performance. We reported some of the experimental result, which is better performance than the previous system.
목차
Abstract
1. Introduction
2. Related works
3. Our proposal for a Music Recommender System
4. The Environment of Implementation and Experiment and Evaluation
5. Conclusions
References
참고문헌 (8)
참고문헌 신청
Balabanovic, M. and Shoham, Y., “Fab : Content-based, Collaborative Recommender”, Communication of the Association of Computing Machinery, Vol. 40, No. 3, 1997, pp. 66-72.
Griffiths, T. and A. Yuille, “A primer on probabilistic inference, Trends in Cognitive Sciences Supplement to special issue on Probabilistic Models of Cognition”, Vol. 10, No. 7, 2006, pp. 1-11.
Hand, D., Mannila, H., and Smyth, P., “Maintenance of Discovered Association Rules in Large Databases : An Incremental Updating Technique”, the International Conference on Data Engineering, 1996, pp. 106-114.
Hand, D., Mannila, H., and Smyth, P., “Principles of Data Mining”, 2001, The MIT Press.
Herlocker, J. L., Kosran, J. A., Borchers, A., and Riedl, J., “An Algorithm Framework for Performing Collaborative Filtering”, Proceedings of the 1999 Conference on Research and Development in Information Research and Development in Information Retrival, 1999.
Griffiths, T. and A. Yuille, “A primer on probabilistic inference, Trends in Cognitive Sciences Supplement to special issue on Probabilistic Models of Cognition”, Vol. 10, No. 7, 2006, pp. 1-11.
Hand, D., Mannila, H., and Smyth, P., “Maintenance of Discovered Association Rules in Large Databases : An Incremental Updating Technique”, the International Conference on Data Engineering, 1996, pp. 106-114.
Hand, D., Mannila, H., and Smyth, P., “Principles of Data Mining”, 2001, The MIT Press.
Herlocker, J. L., Kosran, J. A., Borchers, A., and Riedl, J., “An Algorithm Framework for Performing Collaborative Filtering”, Proceedings of the 1999 Conference on Research and Development in Information Research and Development in Information Retrival, 1999.
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UCI(KEPA) : I410-ECN-0101-2016-005-002786736