Abstract: The present disclosure provides a system for generating and matching multimodal embeddings for business recommendations. The system includes a processor and a memory storing instructions that, when executed by the processor, cause the system to generate multimodal embeddings for vendor-payor network entities. The multimodal embeddings comprise document embeddings, payor-vendor network interaction embeddings, payor segment embeddings, and vendor segment embeddings. The system stores the multimodal embeddings in a vector database, retrieves embeddings from the vector database in response to a query for an entity, matches and reranks retrieved results against the query, and implements sanity checks to minimize false positives in recommendations.

Abstract: Particular embodiments relate to personalized document field prediction based on user behavior and feature generation. Specifically, various embodiments have the technical effect of improved accuracy with respect to field/entity value prediction (e.g., predicting that the amount due is X via a Gradient Boosting Model) relative to document processing technologies by learning through user behavior data or feedback (e.g., through continuous reinforcement learning from human feedback (RLHF)). This is at least partially because of the technical solution of accessing or generating unique features from one or more documents previously used by a user.