11. A computer-implemented process for constructing music playlists through clustering of music similarity vectors, comprising steps for: constructing a set of multidimensional music similarity vectors from a sparse graph of music similarities representing interrelationships between a plurality of music objects; recursively forming clusters of at least one of the music similarity vectors as a function of computed distances between the music similarity vectors in multidimensional space; wherein recursively forming the clusters further comprises steps for: initially placing each music similarity vector into a unique cluster, identifying a music similarity vector having a largest number of neighboring music similarity vectors within a minimum distance threshold, and forming a new cluster from the identified music similarity vector and the identified neighboring music similarity vectors, removing any music similarity vectors already added to a new cluster from further cluster considerations, and repeating the formation of new clusters and removing of music similarity vectors from further cluster consideration until all music similarity vectors have been assigned to a new cluster of at least one music similarity vector; and generating at least one music playlist by automatically populating the playlist with at least one music object corresponding to music similarity vectors from clusters corresponding to the music similarity vectors of at least one music object selected via a user interface.
11. A computer-implemented process for constructing music playlists through clustering of music similarity vectors, comprising steps for: constructing a set of multidimensional music similarity vectors from a sparse graph of music similarities representing interrelationships between a plurality of music objects; recursively forming clusters of at least one of the music similarity vectors as a function of computed distances between the music similarity vectors in multidimensional space; wherein recursively forming the clusters further comprises steps for: initially placing each music similarity vector into a unique cluster, identifying a music similarity vector having a largest number of neighboring music similarity vectors within a minimum distance threshold, and forming a new cluster from the identified music similarity vector and the identified neighboring music similarity vectors, removing any music similarity vectors already added to a new cluster from further cluster considerations, and repeating the formation of new clusters and removing of music similarity vectors from further cluster consideration until all music similarity vectors have been assigned to a new cluster of at least one music similarity vector; and generating at least one music playlist by automatically populating the playlist with at least one music object corresponding to music similarity vectors from clusters corresponding to the music similarity vectors of at least one music object selected via a user interface. 12. The computer-implemented process of claim 11 wherein the step for constructing the set of multidimensional music similarity vectors is performed by a remote server computer.