Abstract: The present technology pertains to steering a playlisting service toward media items that are likely to receive positive feedback from a user operating a client device. The present technology permits a request to play media items without requiring an input context. A playlist service can begin to receive feedback on the playback of the media items and the received playback can be utilized by a steering service in response to a steering request to identify media items for playback that are likely to receive positive feedback based on the feedback received on a sequence of previously played media items.

Abstract: Generating a customized playlist may include identifying a user account for which a playlist is to be generated, where the user account is associated with a user listening history, a taste profile, and a social profile, identifying one or more friend accounts linked to the user account based on the social profile, obtaining an indication of a plurality of songs associated with one or more of the friend accounts, determining a listening history for the one or more plurality of songs based on the friend accounts, obtaining a subset of the plurality of songs based on the listening history of the plurality of songs, and generating a playlist from the subset of the plurality of songs.

Abstract: The present technology pertains to steering a playlisting service toward media items that are likely to receive positive feedback from a user operating a client device. The present technology permits a request to play media items without requiring an input context. A playlist service can begin to receive feedback on the playback of the media items and the received playback can be utilized by a steering service in response to a steering request to identify media items for playback that are likely to receive positive feedback based on the feedback received on a sequence of previously played media items.

Abstract: The present technology pertains to automatically context labeling media items with relevant contexts, and further for algorithmically generating high quality playlists built around a context that are personalized to a profile of an account. This is accomplished by combining data from observed playlists, and data representing intrinsic properties of media items to predict contexts for media items.

Abstract: The present technology pertains to automatically contextlabeling media items with relevant contexts, and further for algorithmically generating high quality playlists built around a context that are personalized to a profile of an account. This is accomplished by combining data from observed playlists, and data representing intrinsic properties of media items to predict contexts for media items.

Abstract: Generating a customized playlist may include identifying a user account for which a playlist is to be generated, where the user account is associated with a user listening history, a taste profile, and a social profile, identifying one or more friend accounts linked to the user account based on the social profile, obtaining an indication of a plurality of songs associated with one or more of the friend accounts, determining a listening history for the one or more plurality of songs based on the friend accounts, obtaining a subset of the plurality of songs based on the listening history of the plurality of songs, and generating a playlist from the subset of the plurality of songs.

Abstract: The present technology pertains to steering a playlisting service toward media items that are likely to receive positive feedback from a user operating a client device. The present technology permits a request to play media items without requiring an input context. A playlist service can begin to receive feedback on the playback of the media items and the received playback can be utilized by a steering service in response to a steering request to identify media items for playback that are likely to receive positive feedback based on the feedback received on a sequence of previously played media items.

Abstract: The present technology pertains to automatically contextlabeling media items with relevant contexts, and further for algorithmically generating high quality playlists built around a context that are personalized to a profile of an account. This is accomplished by combining data from observed playlists, and data representing intrinsic properties of media items to predict contexts for media items.

Abstract: User created playlists can be analyzed to create a statistical language model indicating the likelihood that a particular sequence of content attributes will be found in a playlist created by a user, as well as the likelihood of any sequence of one or more content attributes following a playlist or partial playlist created by a user. The language model can be used to generate a recommended content attribute sequence based on a partial playlist of one or more content items. A recommended content item sequence that will be pleasant to a user when added to the partial playlist can be selected based on the recommended content attribute sequence.

Abstract: The present technology pertains to automatically providing refreshed versions of personalized playlists of media items based on a refresh schedule. The automatically refreshed personalized playlists can be automatically downloaded to a client device associated with the user account. In some embodiments an animation can be presented to show that a playlist was automatically refreshed. The automatically refreshed personalized playlist can be created based on an assumption that playlists are a collection of media items that go well together in a collection.

Abstract: User created playlists can be analyzed to create a statistical language model indicating the likelihood that a particular sequence of content attributes will be found in a playlist created by a user, as well as the likelihood of any sequence of one or more content attributes following a playlist or partial playlist created by a user. The language model can be used to generate a recommended content attribute sequence based on a partial playlist of one or more content items. A recommended content item sequence that will be pleasant to a user when added to the partial playlist can be selected based on the recommended content attribute sequence.

Abstract: Soccer training shoes are provided having ball contact elements that cause a soccer ball to more readily bounce away from the foot of the user. In one aspect of the invention, a training device is provided for temporary attachment to the shoe of a soccer player for providing hard inside and outside ball contact elements that cause a soccer ball to more readily bounce away from the foot of the user and thus promote the development of the "soft feel" that is desirable for catching, trapping and dribbling activities. The training device is essentially in the form of a web of straps that encompass the toe and instep portion of a soccer shoe and with forward, intermediate and rear transverse straps each being provided with a relatively hard ball contact element for location along the vamp of a soccer shoe in the immediate region of the inside and outside edges of the sole of the shoe.