Browse > Article
http://dx.doi.org/10.5391/JKIIS.2009.19.4.504

A Personalized Music Recommendation System with a Time-weighted Clustering  

Kim, Jae-Kwang (성균관대학교 전자전기컴퓨터공학과)
Yoon, Tae-Bok (성균관대학교 전자전기컴퓨터공학과)
Kim, Dong-Moon (성균관대학교 전자전기컴퓨터공학과)
Lee, Jee-Hyong (성균관대학교 전자전기컴퓨터공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.19, no.4, 2009 , pp. 504-510 More about this Journal
Abstract
Recently, personalized-adaptive services became the center of interest in the world. However the services about music are not widely diffused out. That is because the analyzing of music information is more difficult than analyzing of text information. In this paper, we propose a music recommendation system which provides personalized services. The system keeps a user's listening list and analyzes it to select pieces of music similar to the user's preference. For analysis, the system extracts properties from the sound wave of music and the time when the user listens to music. Based on the properties, a piece of music is mapped into a point in the property space and the time is converted into the weight of the point. At this time, if we select and analyze the group which is selected by user frequently, we can understand user's taste. However, it is not easy to predict how many groups are formed. To solve this problem, we apply the K-means clustering algorithm to the weighted points. We modified the K-means algorithm so that the number of clusters is dynamically changed. This manner limits a diameter so that we can apply this algorithm effectively when we know the range of data. By this algorithm we can find the center of each group and recommend the similar music with the group. We also consider the time when music is released. When recommending, the system selects pieces of music which is close to and released contemporarily with the user's preference. We perform experiments with one hundred pieces of music. The result shows that our proposed algorithm is effective.
Keywords
Music recommendation; Time-weighted Clustering; Improved K-means algorithm;
Citations & Related Records
연도 인용수 순위
  • Reference
1 H.C. Chen, A.L.P. Chen. 'A Music Recommendation System based on Music Data Grouping and User Interests,' Proc. of CIKM, pp. 231-238, 2001
2 G. Tzanetakis, P. Cook, 'Musical genre classification of audio signals,' IEEE Trans. Speech and Audio Processing, pp. 293-302, 2002
3 Y.S. Kim, S. Mitra, 'Integrated Adaptive Fuzzy clustering (IAFC) Algorithm,' Proc. of the Second IEEE International Conference on Fuzzy Systems, Vol. 2, pp. 1264-1268, San Francisco, 1993
4 B. Yapriady, A.L. Uitdenbogerd, 'Combining demographic data with collaborative filtering for automatic music recommendation,' Lecture notes in computer science, Springer, pp. 201-207, 2005
5 유지오, '퍼지 베이지안 네트워크와 효용성 이론을 사용한 상황 기반 음악 추천,' 연세대학교, Ph.D thesis, Feb, 2006
6 R.O. Duda, P.E. Hart, D.G. Stork, 'Pattern classification 2nd,' Wileyinterscience, 2001
7 P. Cano, M. Koppenberger, N. Wack, 'Content-based Music Audio Recommendation,' Proc. of ACM International Conference on Multimedia, pp. 211-212, 2005
8 B. Logan. 'Music Recommendation from Song Sets,' Proc. of ISMIR, pp. 425-428, 2004