System and method for real-time generation and optimization of personalized cause-aligned content

A system for real-time generation of personalized content for a user, the system comprising:

• a processor;
• and
• a memory storing instructions that, when executed by the processor, cause the system to:
• receive a request comprising a brand identifier and a user identifier;
• retrieve, from a pre-computed low-latency lookup table, a plurality of cause records associated with the brand identifier, wherein each cause record in the low-latency lookup table is associated with a brand-cause compatibility score;
• compute, using a multilayered machine learning model, a plurality of cause-user relevance scores, wherein each cause-user relevance score represents a relevance between the user identifier and a respective cause record from the retrieved plurality of cause records, wherein the plurality of cause-user relevance scores is computed by:
• receiving, via a sensory layer of the multilayered machine learning model, data associated with one or more brands and one or more causes, wherein the received data includes textual data and image data;
• processing the textual data and image data to determine context by:
• tokenizing the textual data into one or more tokens, generating first feature vectors for the one or more tokens by iteratively refining token embeddings through multiple self-attention layers and feed-forward neural network layers of a transformer-based natural language model;
• dividing the image data into fixed-size patches, generating second feature vectors for the fixed-size patches by encoding patch embeddings through multiple self-attention layers of a Vision Transformer (ViT);
• and
• combining the first feature vectors and the second feature vectors into a multimodal context representation;
• executing, via a cognitive layer of the multilayered machine learning model, an Audience Response Model comprising a long short-term memory (LSTM) network with an attention mechanism on the multimodal context representation, historical audience response sequences associated with the user identifier, and prior engagement metrics corresponding to similar brand-cause campaigns to generate an engagement likelihood representation indicating predicted audience interaction with each respective cause record;
• and
• generating, by an alignment scoring model via an executive layer of the multilayered machine learning model, a brand-cause compatibility score based on the engagement likelihood representation;
• select a target cause record from the plurality of cause records, wherein the target cause record has a highest cause-user relevance score among the plurality of cause-user relevance scores, and wherein the highest cause-user relevance score exceeds a predetermined threshold;
• generate, in real-time, the personalized digital content for the user identifier, by combining brand-related content associated with the brand identifier and cause-related content associated with the target cause record;
• transmit a personalized advertisement for rendering on a device associated with the user identifier;
• track user engagement data associated with the personalized digital content;
• and
• continuously train the multilayered machine learning model based on the user engagement data to refine at least the cause-user relevance scores and the brand-cause compatibility score.