Ji Yun Chung
ABSTRACT
Recently, the popularity of Korean pop music (or K-pop) has been increasing, due to technological developments in digital devices. According to the IFPI, 59% of Koreans regularly listen to music on their smartphones and other related devices [1]. Thus, the present study proposes a music recommendation model that predicts listeners' music preferences and recommends customized music lists. For this purpose, two pilot tests (with one participant in each test) were conducted and linear regression analysis was performed by using the Tensorflow application. The first test determined the participant's song preferences, based on a sample of 200 K-pop songs, while the second test added a neutral response option when considering a sample of 200 additional K-pop songs. The results indicate that the prediction accuracy of the participant's song preferences in the first test was 71.5%. However, after adding the neutral response option in the second pilot test, the prediction accuracy increased to 84.0%. This model can be used to predict the music preferences of listeners on a wider scale.
- IFPI. 2017. Connecting with music. Music Consumer Insight Report. (Sep.2017), IFPI, London.Google Scholar
- J.Y. Chung. 2017. A study on the Trend of Korean Pop Music Preference Through Digital Music Market. Jornal of Digital Contents Society. 18, 6 (Oct. 2017), 1025--1032.Google Scholar
- Adomavicius, G., Tuzhilin A. 2005. 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. 17, 6(Apr.2005), 734--749. Google ScholarDigital Library
- M. A. Lee. 2012. Music Recommendation System based on Radio Episode Analysis. Master Thesis. Seoul National University.Google Scholar
- G. Linden, B Smith, J York.2003. Amazon. com recommendations: Item-to-item collaborative filtering, IEEE Internet Computing. 7, 1(Jan. 2003), 76--80. Google ScholarDigital Library
- Pazzani. M.J. 1999. A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13, 5--6, 393--408(1999). Google ScholarDigital Library
- Marko Balabanovic, YoavShoham, 1997. Fab:Content-Based, Collaborative Recommendation. Communications of the ACM. 40, 3(Mar. 1997). Google ScholarDigital Library
- J. K. Kim, Y. H. Cho, S. T. Kim, H. K. Kim. 2005. A Personalized Recommender System for Mobile Commerce Applications. Asia Pacific Journal of Information Systems 15, 3(Sep.2005). UCI= http://uci.or.kr/G704-000077.2005.15.3.007Google Scholar
- G. R. Kim, J. H. Byun, N. M. Moon. 2011. Collaborative Filtering Design Using Genre Similarity and Proffered Genre. Journal of the Korea society of computer and information. 16, 4(Apr.2011), 159--168. UCI= http://uci.or.kr/G704-001619.2011.16.4.012Google Scholar
Index Terms
- Music recommendation model by analysis of listener's musical preference factor of K-pop
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