In the last twenty years, there has been an amazing transformation in the world of music. Portable listening devices have advanced from the Sony Walkman that allowed you to carry ten songs in your pocket to the latest iPhone that can put millions of songs in your pocket via music subscription services such as Spotify or Rhapsody. Twenty years ago a typical personal music collection numbered around a thousand songs. Today, a music listener has access to millions of songs, drawn from all styles and genres from all over the world. The seemingly infinite choice today's music listener faces can lead to a rich music listening experience, but only if the listener can find music that they want to listen to. Traditionally, music recommender systems have focused on the somewhat narrow task of attempting to predict a set of new artists or tracks for purchase or listening. Commerce sites like iTunes use music recommendation as a way to increase sales. Internet radio sites like Pandora use music recommendation as a way to offer personalized radio to millions of listeners. The success of music recommendation at iTunes and Pandora has led some to suggest that 'music recommendation is solved'. Indeed, for narrow use cases like improving sales in a mainstream music store, or for creating satisfactory personalized radio streams, music recommendation may be good enough. However, this does not mean that music recommendation is solved. As music listeners spend more time interacting with multi-million song music collections, the need for tools that help listeners manage their listening will become increasingly important. Tools for exploring and discovering music especially in the long tail, tools for organizing listening, tools for creating interesting playlists, tools for managing group listening will all be essential to the music listening experience. Music recommendation technologies will be critical to building these tools. The WOMRAD workshop focuses on next generation of music recommender systems. Accepted papers fall into five categories:
• • • • •
Time Dependency - 1 paper - explorations in temporal patterns of music listening Social Tagging - 3 papers - how semantic tags can be used to explain, compare and steer music recommender systems Human-Computer Interaction - 2 papers - how music listeners interact with music and music recommender systems Content-based Recommendation - 2 papers - techniques for recommendation base on audio content Long Tail - 2 papers - how can systems make effective recommendations of new or unpopular content We are pleased to offer this selection of papers and hope that it serves as evidence that there is much interesting and fruitful research to be done in the area of music recommendation and discovery. We offer our thanks to all of the authors who submitted papers to this workshop.