System and method for generating personalized recommendations

A computing system for generating personalized skincare product recommendations, comprising:

• an interface configured to receive user data that includes metadata and biologic data of a person;
• and
• one or more neural networks (NNs) to perform operations that include:
• generating multi-omic heatmaps that represent correlations among the biologic data and omic data;
• determining, using a first one of the one or more neural networks that is trained on a knowledge graph derived from multi-omic datasets, correlations between biological markers identified from the biologic data and adverse reactions to one or more skincare product ingredients;
• generating a prompt context for a large language model (LLM) from the metadata and the multi-omic heatmaps, wherein the LLM is configured as a Biologically Embodied Multilayer Analysis System (BeMAS) with a Mixture of Experts (MoE) architecture;
• and
• producing, using the prompt context and the LLM a personalized skincare product recommendation for the person that excludes products predicted to cause the adverse reactions.

A computing system, comprising:

• one or more processors configured to perform operations, wherein the operations include:
• processing metadata and biological sequencing data of a person, wherein the processing includes integrating contextual information of the metadata with the biological sequencing data;
• generating multi-omic heatmaps representing sequencing data from the biologic data, wherein the multi-omic heatmaps identify biological markers according to knowledge graphs derived from multi-omic datasets;
• identifying correlations between the biological markers and adverse reactions to one or more ingredients of one or more beauty products;
• and
• generating a personalized beauty product recommendation for the person according to the correlations and the metadata, wherein the personalized beauty product recommendation excludes the one or more beauty products associated with the adverse reactions.