Abstract: Described are systems and processes for identifying relevant media content for users in response to natural language requests. The media content may be formed as a playlist of music. Requests may be sent to various models to obtain results, such as a large language model (LLM) and a local search model. Results may be selected from one or both models. The results may be used to fetch media content and provide user interfaces with a playlist with the media content to a user that submitted the request. User interfaces may include selectable search terms suggested for the user and animations during processing of the request.

Abstract: Described herein are approaches for generating a new queue based on an existing queue. This may include receiving a request to transfer the existing queue from a first device to a second device. A set of move criteria may be evaluated using a playback context, a user profile, a configuration associated with the second device, and/or a level of access constraints. Depending on the results of the evaluation, the existing queue may be completely reformulated to define the new queue. The second device may then be instructed to play the new queue.

Abstract: This disclosure is directed to a system and method that generates personalized recommendations of media items for customers based on a customer's preference for familiar or novel media items. A customer can be associated with user data that can include data such as a customer's purchase data, shopping data, etc. The user data can be used to determine media item data that is associated with the customer. Based on the customer's media item data, a familiarity score can be generated that indicates a media consumption preference of the customer. Using the familiarity score, a recommendation identifying a media item can be generated for the customer.