  Many tasks in music information retrieval, such as recommendation, and
playlist generation for online radio, fall naturally into the query-by-example
setting, wherein a user queries the system by providing a song, and the system
responds with a list of relevant or similar song recommendations. Such
applications ultimately depend on the notion of similarity between items to
produce high-quality results. Current state-of-the-art systems employ
collaborative filter methods to represent musical items, effectively comparing
items in terms of their constituent users. While collaborative filter
techniques perform well when historical data is available for each item, their
reliance on historical data impedes performance on novel or unpopular items. To
combat this problem, practitioners rely on content-based similarity, which
naturally extends to novel items, but is typically out-performed by
collaborative filter methods.
  In this article, we propose a method for optimizing contentbased similarity
by learning from a sample of collaborative filter data. The optimized
content-based similarity metric can then be applied to answer queries on novel
and unpopular items, while still maintaining high recommendation accuracy. The
proposed system yields accurate and efficient representations of audio content,
and experimental results show significant improvements in accuracy over
competing content-based recommendation techniques.
