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: A content management system and/or client device can enable a user to initiate a quick play mode where a content category and content medium are selected for the user. A client device and/or a content management system can select a content medium for a user based on one or more factors, such as the content category. Certain content categories of content can be preferably delivered in certain content mediums. In some embodiments, a content management system and/or client device can select a content medium for a user based on contextual data gathered from the user. Contextual data can be data describing the user's current state and/or environment. For example, contextual data can include data such as the time of day, geographic location, etc.

Abstract: Techniques for suggesting media assets, the technique including: requesting a set of candidate media assets for a set of user media items based on a knowledge graph metadata network describing the set of user media items; receiving metadata for the set of candidate media assets; determining one or more sets of ranked media assets based on the received metadata; and outputting the determined one or more sets of ranked media assets.

Abstract: A content management system and/or client device can enable a user to initiate a quick play mode where a content category and content medium are selected for the user. A client device and/or a content management system can select a content medium for a user based on one or more factors, such as the content category. Certain content categories of content can be preferably delivered in certain content mediums. In some embodiments, a content management system and/or client device can select a content medium for a user based on contextual data gathered from the user. Contextual data can be data describing the user's current state and/or environment. For example, contextual data can include data such as the time of day, geographic location, etc.

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: A content management system and/or client device can enable a user to initiate a quick play mode where a content category and content medium are selected for the user. A client device and/or a content management system can select a content medium for a user based on one or more factors, such as the content category. Certain content categories of content can be preferably delivered in certain content mediums. In some embodiments, a content management system and/or client device can select a content medium for a user based on contextual data gathered from the user. Contextual data can be data describing the user's current state and/or environment. For example, contextual data can include data such as the time of day, geographic location, etc.

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.