Media recommendation using internet media stream modeling

Abstract

Media item recommendations, such as music track recommendations, may be made using one or more models generated using data collected from a plurality of media stream sources, such as, for example, Internet radio stations. In an initial, bootstrapping phase, data about media items and media stream playlists of media stream sources may be used to generate a model, which comprises latent factor vectors, or learned profiles, of media items, e.g., tracks, artists, etc. Such a bootstrapping phase may be performed without user data, such as user playlists and/or user feedback, to generate a model that may be used to make media item recommendations. As user data becomes available, e.g., as users of a recommendation service provide user data, the user data may be used to supplement and/or update the model and/or to create user profiles.