In the age of internet radio and digital music storage, millions of songs are available to listeners at a click of a button. With this huge amount of songs to choose from and with the taste of music varying from person to person, it becomes a difficult task for the user to select the "right" song for the right moment. Various playlist generation algorithms have been developed in which playlists are lists of sequentially ordered tracks, represent a possible solution to this issue and help users explore this huge collection of songs available. However the current schemes mostly focus on finding the closest songs to a seed song. We feel that a better playlist should not only play the closest songs but also serve to make it a discovery experience with lots of varieties. Therefore we will first study the community structure of our song similarity network. Then we will apply personalized PageRank with consideration of the community structure to generate playlist.
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