RNDM: A Random Walk Method for Music Recommendation by Considering Novelty, Diversity, and Mainstream

Abstract

Music recommendation systems automatically suggest a track to a user according to the user's preference. Thus, effectiveness information mining approach is necessarily required to analyze and measure the multiple attributes which could affect the users' preference such that more personalized recommendation can be achieved. However, traditional music recommendation systems rarely consider the multiple attributes of users' preference, leading to a misguided recommendation. In this paper, we propose a novel music recommendation approach, named RNDM. RNDM which employs three computational attributes to describe the user's music taste: novelty, diversity and mainstream. Based on these three attributes, a virtual friendship of each user is constructed by using a modified random walk algorithm. Finally, more personalized recommendations can be achieved by exploiting the music preferences of both the target user and his virtual friends. Compared with the state-of-the art music recommendation approaches, the experimental results show that RNDM significantly improves the accuracy of recommendation and provides more personalized music recommendation for users.