Abstract: Video content recommendations are described. In embodiments, a request for a recommendation of video content is received from a client device, and the recommendation includes identifiers of video assets for an optimal viewing schedule for a user. A utility of each video asset can be determined that indicates a social value of a video asset to the user. A time relevance of each video asset can also be determined that indicates how soon the user may select to watch the video asset, based at least in part on the social value of the video asset. The optimal viewing schedule can then be generated based on the utility of each video asset and the time relevance that is associated with each video asset. The optimal viewing schedule includes recommended video assets that, when watched by the user, provide the most social value in the shortest amount of viewing time.

Abstract: Various embodiments provide a transition service that provisions transition metadata files for use by client devices. The transition metadata files are configured to provide song-specific transitions between songs. In at least some embodiments, the transition service receives a request for a transition metadata file associated with at least an entry song from a client device. The transition service then selects an associated transition metadata file and transmits the associated transition metadata file to the client device for execution. When executed during the playback of the entry song at the client device, the transition metadata file provides a transition from the entry song to an exit song. In at least some embodiments, the transition metadata files are created by a DJ service that provides transitions from specific entry songs to specific exit songs.

Abstract: Social network reward techniques are described. In an implementation, a method implemented by at least one computing device includes obtaining data that describes users' interaction with a social network service and determining influence of one or more said users on other said users via the social network service from the obtained data. A reward is computed based on the determined influence for at least one said user.

Abstract: Content recommendation techniques are described. In an implementation, content preferences for a group are determined by identifying an intersection of content preferences for individual users in the group. Content that is currently available for presentation is recommended based on the intersection by comparing the content preferences for the group with metadata for the content that is available for presentation.