Implementing a hybrid Recommender System in Julia
José Quenum, marthin thomas
This talk discusses a hybrid recommender system implemented in Julia for applicant preselection for a job. 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.
Julia for High-Performance Computing
Main Room 4