Mining Music Social Networks for Automating Social Music Services

Community-driven services compile data provided by the community members, for instance playlists in Web 2.0 music sites. We show how this data can be analysed and knowledge about sequential associations between songs and artists can be discovered. While most of this kind of analysis focus on (symmetric) similarity measures, we intend to discover which songs can “musically follow” others, focusing on the sequential nature of this data in a database of over 500,000 playlists. We obtain a song association model and an artists association model, we evaluate these models comparing the results with other similaritybased analysis, and finally we show how these models can be used to automatically schedule sequences of songs in a social Web radio service.