2025-07-23 –, Main Room 4
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.
In this talk, we present the implementation of a hybrid recommender system that helps preselect candidates for a job application. We discuss the preprocessing of the data following NLP techniques and building on various libraries, including TextAnalysis, Embeddings and MLJ. The input information (applicant metadata and job adverts) is aggregated into a heterogeneous graph, later converted into a GNN using GraphNeuralNetworks. The underlying model supporting the recommendations combines several graph convolutional layers and a transformer (encoder and decoder). To make the model's training more efficient, we rely on the Distributed and ClusterManagers libraries. Note that our preprocessing and training steps are implemented using a supercomputer. We present the implementation and the job submission details.
José G. Quenum is a Namibia University of Science and Technology (NUST) professor. His research interests include coordination models in multi-agent systems, distributed systems, artificial Intelligence and Big Data infrastructure.
