Towards Gathering and Mining Last.fm User-Generated Data
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Online musical social communities are becoming more and more popular as time goes by. Nowadays, these social communities give folksonomy systems to their users, allowing them to play an active role by dictating musical tastes, popularity of artists and hit songs like it never happened before. Since it is the people, and especially the fans, that really determine how famous an artist can be or how a song can impact their musical tastes, these Web social communities are the ultimate form of capturing the perspective of the fans. Although the widespread musical communities give us more informatio...
Online musical social communities are becoming more and more popular as time goes by. Nowadays, these social communities give folksonomy systems to their users, allowing them to play an active role by dictating musical tastes, popularity of artists and hit songs like it never happened before. Since it is the people, and especially the fans, that really determine how famous an artist can be or how a song can impact their musical tastes, these Web social communities are the ultimate form of capturing the perspective of the fans. Although the widespread musical communities give us more information about music, there is still too much to learn about it. We are trying to bring results to this information and find relations, patterns and knowledge that influence the music culture. Our studies focused on the popular music social community Last.fm, where we have access to millions of entries concerning music and artists. Our goal in this project is to collect Last.fm musical data and learn interesting patterns and knowledge about music from the past choices of fans, being the prediction of the popularity of artists our most ambitious goal.
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