Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
The predictive power of online chatter
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Proceedings of the 15th international conference on World Wide Web
Predicting success from music sales data: a statistical and adaptive approach
Proceedings of the 1st ACM workshop on Audio and music computing multimedia
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Can all tags be used for search?
Proceedings of the 17th ACM conference on Information and knowledge management
Trend detection in folksonomies
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
On the relationship between novelty and popularity of user-generated content
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
On the Relationship between Novelty and Popularity of User-Generated Content
ACM Transactions on Intelligent Systems and Technology (TIST)
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What makes a song to a chart hit? Many people are trying to find the answer to this question. Previous attempts to identify hit songs have mostly focused on the intrinsic characteristics of the songs, such as lyrics and audio features. As social networks become more and more popular and some specialize on certain topics, information about users' music tastes becomes available and easy to exploit. In the present paper we introduce a new method for predicting the potential of music tracks for becoming hits, which instead of relying on intrinsic characteristics of the tracks directly uses data mined from a music social network and the relationships between tracks, artists and albums. We evaluate the performance of our algorithms through a set of experiments and the results indicate good accuracy in correctly identifying music hits, as well as significant improvement over existing approaches.