A Fair Approach to Music Recommendation Systems Based on Music Data Grouping

• Published in: IAENG International Journal of Computer Science Vol. 38; no. 4; pp. 418 - 427
• Main Authors: Chang, Ye-In, Wu, Chen-Chang, Tsai, Meng-Chang
• Format: Journal Article
• Language: English
• Published: 12.11.2011
• Subjects: Computer simulation; Density; Mathematical analysis; Mathematical models; Music
• ISSN: 1819-656X

How recommending the music that user is interested in from a wide variety of music is the development intentions of the music recommendation system MRS (Music Recommendation System). Chen et al. have proposed the Content-based (CB) and Collaborative (COL) methods for music recommendation. The CB method is to recommend the music objects that belong to the music groups the user is recently interested in and the COL method is to provide unexpected findings due to the information sharing between relevant users. But the CB method will lead to the result that the group weight of music group B which appears once in the later transaction is larger than the group weight of the music group A which appears many times in the earlier transaction. The COL method will lead to the result that the supports of the groups which have different densities are the same, and then the users may be grouped together. Therefore, in this paper, to be fair, we propose the TICI (Transaction-Interest-Count-Interest) method to improve the CB method, and propose the DI (Density-Interest) method to improve the COL method. Our DI method calculates the supports of music groups and consider the distributions of appearances of the music group. From our simulation results, we show that our TICI method could provide better performance than the CB method. Moreover, our DI method also could provide better performance than the COL method.