Abstract: Various implementations relate to methods, systems, and computer readable media for providing conversational recommendations of virtual experiences within a virtual platform. According to one aspect, a computer-implemented method includes obtaining a natural language input from a user comprising a request for a recommendation of virtual experiences, analyzing the natural language input to determine a user intent and a plurality of user preferences, and generating a set of candidate recommendations based on the user intent and the plurality of user preferences. A ranking of the set of candidate recommendations is generated using weighted aggregation of evaluation metrics produced by a plurality of AI models. A set of refined recommendations is generated by modifying the ranking using a plurality of contextual cues across the plurality of AI models, and the set of refined recommendations is transmitted to the user.

Abstract: A computer-implemented method provides automatic conversational recommendations. The computer-implemented method includes prompting an LLM to process a user request based on a formatted structure into a formatted request. The user request is natural language text. The computer-implemented method includes executing a plurality of tools by a tool policy to obtain item information from an item repository based on the formatted request. The computer-implemented method includes prompting the LLM with the user request and the item information to generate a list of items in response to the user request. The computer-implemented method includes causing a graphical user interface to display one or more items from the list of items.

Abstract: Techniques for using a quantized and/or fused model are described. In some examples, a service is to receive a request to use a trained model, the request including input data; apply a trained model to the input data, the application of the trained model includes fusing one or more matrix multiplication operations with element-wise operations; and output a result from the trained model.

Abstract: Systems, methods, and non-transitory computer readable media can obtain dynamic signals associated with content items. A machine learning model can be trained based on the dynamic signals associated with the content items. A classification for a content item can be determined based on the machine learning model.

Abstract: Systems, methods, and non-transitory computer readable media can obtain dynamic signals associated with content items. A machine learning model can be trained based on the dynamic signals associated with the content items. A classification for a content item can be determined based on the machine learning model.