Artificial intelligence job recommendation neural network machine learning training based on embedding technologies and actual and synthetic job transition latent information

Abstract

An artificial intelligence (AI) job recommender system and methods implement neural network machine learning by generating and utilizing actual and synthetic training data to identify, learn, and apply latent job-to-job transition information and trends to improve job recommendations. The AI job recommender system and method represent technological advances that, for example, identify data representations, identify multiple instances of latent information in actual data, develop synthetic training data, create a directed graph from latent, directional information, embed the directed graph into a vector space, and apply machine learning algorithms to technologically advance and transform a machine into a specialized machine that learns and improves job recommendations across the vector space.