Abstract: The disclosed computer-implemented method may include receiving, by a computing device, a request to generate an explanation of a recommendation to a user, wherein the request includes a recommended title and a profile history of the user. The method may include selecting a set of previous titles from the profile history that exceeds a threshold similarity score. Additionally, the method may include extracting, by fetching descriptive information about each previous title, a set of keywords. The method may also include automatically prompting, based on the set of keywords, a generative artificial intelligence (AI) model to generate the explanation of the recommendation using the set of previous titles. Furthermore, the method may include performing a safety check to filter the generated explanation. Finally, the method may include sending the recommendation and the explanation to a client device of the user. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include receiving, by a computing device, a request to generate an explanation of a recommendation to a user, wherein the request includes a recommended title and a profile history of the user. The method may include selecting a set of previous titles from the profile history that exceeds a threshold similarity score. Additionally, the method may include extracting, by fetching descriptive information about each previous title, a set of keywords. The method may also include automatically prompting, based on the set of keywords, a generative artificial intelligence (AI) model to generate the explanation of the recommendation using the set of previous titles. Furthermore, the method may include performing a safety check to filter the generated explanation. Finally, the method may include sending the recommendation and the explanation to a client device of the user. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A content server generates media playlists for social events. The content server receives a seed value describing desired characteristics of media to present during a social event, and event parameters describing conditions for selecting media items to generate a media playlist for the social event. The content server selects media items based on the event parameters and the seed value, and uses the selected media items to generate the media playlist for the social event. The media items in the playlist are retrieved from a media database and provided to a media player for presentation at the social event.

Abstract: A content server generates media playlists for social events. The content server receives a seed value describing desired characteristics of media to present during a social event, and event parameters describing conditions for selecting media items to generate a media playlist for the social event. The content server selects media items based on the event parameters and the seed value, and uses the selected media items to generate the media playlist for the social event. The media items in the playlist are retrieved from a media database and provided to a media player for presentation at the social event.

Abstract: A content server generates media playlists for social events. The content server receives a seed value describing desired characteristics of media to present during a social event, and event parameters describing conditions for selecting media items to generate a media playlist for the social event. The content server selects media items based on the event parameters and the seed value, and uses the selected media items to generate the media playlist for the social event. The media items in the playlist are retrieved from a media database and provided to a media player for presentation at the social event.

Abstract: A media service server for streaming media items with similar media features receives a plurality of media items, where each media item of the plurality of media item is labeled with one or more media features characterizing the media item. Audio content features from the plurality of media items are extracted. Correlations between the audio content features and the media features are identified. A set of media items to be analyzed is received. For each media item of the set of media items, a set of media features based on the identified correlations between the audio content features and the plurality of media features is estimated. Each estimated media feature is associated with a confidence score. The set of media items with the estimated media features is provided to one or more experts for expert analysis.

Abstract: A media service server for streaming media items with similar media features receives a plurality of media items, where each media item of the plurality of media item is labeled with one or more media features characterizing the media item. Audio content features from the plurality of media items are extracted. Correlations between the audio content features and the media features are identified. A set of media items to be analyzed is received. For each media item of the set of media items, a set of media features based on the identified correlations between the audio content features and the plurality of media features is estimated. Each estimated media feature is associated with a confidence score. The set of media items with the estimated media features is provided to one or more experts for expert analysis.