Abstract: Disclosure includes a system, method and architecture to monitor media content streams, e.g., streams of digital encoded media data such as without limitation audio and/or video data, from a number of media content streaming providers and identify media content items from those media content items being streamed by the media content streaming providers for inclusion in a personalized media content stream for a user. In a direct streaming approach, the media content items contained in a stream are available while each item is being streamed by the content provider. In a centralized streaming approach, media content items contained in each media content stream that is currently being streamed by content provider are available for a limited window of time, which can be dependent on buffer length.

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.

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.

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.

Abstract: Disclosure includes a system, method and architecture to monitor media content streams, e.g., streams of digital encoded media data such as without limitation audio and/or video data, from a number of media content streaming providers and identify media content items from those media content items being streamed by the media content streaming providers for inclusion in a personalized media content stream for a user. In a direct streaming approach, the media content items contained in a stream are available while each item is being streamed by the content provider. In a centralized streaming approach, media content items contained in each media content stream that is currently being streamed by content provider are available for a limited window of time, which can be dependent on buffer length.