HPC Implementation of a Hybrid Recommender System in Julia
José Quenum, marthin thomas
This talk discusses a hybrid recommender system implemented in Julia for preselecting job applicants. The recommender system is built using a neural network adopting a hybrid architecture that combines convolutional layers of a graph neural network and a transformer (both encoder and decoder). We discuss the preprocessing of applicant metadata and job adverts to generate a heterogeneous graph. Next, we present the recommender as a model and its training using an HPC.
Machine Learning & AI
Machine Learning & AI