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It is not hyperbole to note that a revolution has occurred in the way that we as a society distribute data and information. This revolution has come about through the confluence of Web-related technologies and the approaching universal adoption of internet connectivity. Add to this mix the normalised use of lossy compression in digital music and the increase in digital music download and streaming services; the result is an environment where nearly anyone can listen to nearly any piece of music nearly anywhere. This is in many respects the pinnacle in music access and availability. Yet, a listener is now faced with a dilemma of choice. Without being familiar with the ever-expanding millions of songs available, how does a listener know what to listen to? If a near-complete collection of recorded music is available what does one listen to next? While the world of music distribution underwent a revolution, the ubiquitous access and availability it created brought new problems in recommendation and discovery. In this thesis, a solution to these problems of recommendation and discovery is presented. We begin with an introduction to the core concepts around the playlist (i.e. sequential ordering of musical works). Next, we examine the history of the playlist as a recommendation technique, starting from before the invention of audio recording and moving through to modern automatic methods. This leads to an awareness that the creation of suitable playlists requires a high degree of knowledge of the relation between songs in a collection (e.g. song similarity). To better inform our base of knowledge of the relationships between songs we explore the use of social network analysis in combination with content-based music information retrieval. In an effort to show the promise of this more complex relational space, a fully automatic interactive radio system is proposed, using audio-content and social network data as a backbone. The implementation of the system is detailed. The creation of this system presents another problem in the area of evaluation. To that end, a novel distance metric between playlists is specified and tested. We then conclude with a discussion of what has been shown and what future work remains.

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