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Bayesian Learning through Weight of Listener's Prefered Music Site for Music Recommender System

  • Cho, Young Sung (Department of Computer Software, DongYang Mirae University) ;
  • Moon, Song Chul (Department of Computer Science, Namseoul University)
  • Received : 2015.12.02
  • Accepted : 2016.03.22
  • Published : 2016.03.31

Abstract

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.

Keywords

References

  1. 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.
  2. 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.
  3. 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.
  4. Hand, D., Mannila, H., and Smyth, P., "Principles of Data Mining", 2001, The MIT Press.
  5. 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.
  6. Miyahara, K. and Pazzani, M. J., "Collaborative Filtering with the Simple Bayesian Classifier", In Proc. of the 6th Pacific Rim Int. Conf. on Artificial Intelligence, 2000, pp. 679-689.
  7. Park, H. B., Cho, Y. S., and Ko, Y. H., "Clustering Method of Weighted Preference Using K-means Algorithm and Bayesian Network for Recommender System", Journal of Information Technology Applications and Management, Vol. 20, No. 3, 2013, pp. 219-229.
  8. Pearl, J. and Russel, S., "Bayesian networks, Report(R-277), November 2000, in Handbook of Brain Theory and Neural Networks", M. Arbib, ed, MIT Press, Cambridge, 2001, pp. 157-160.