CareProfSys - Combining Machine Learning and Virtual Reality to Build an Attractive Job Recommender System for Youth: Technical Details and Experimental Data

Abstract

The current article presents CareProfSys - an innovative job recommender system (RS) for youth, which integrates several emergent technologies, such as machine learning (ML) and virtual reality on web (WebVR). The recommended jobs are the ones provided by the well-known European Skills, Competences, Qualifications, and Occupations (ESCO) framework. The machine-learning based recommendation mechanism uses a K-Nearest Neighbors (KNN) algorithm: the data needed to train the machine learning model was based on the Skills Occupation Matrix Table offered by ESCO, as well as on data collected by our project team. This two-source method made sure that the dataset was strong and varied, which made it easier for the model to make accurate recommendations. Each job was described in terms of the features needed by individuals to be good professionals, e.g., skill levels for working with computers, constructing, management, working with machinery and specialized equipment, for assisting and caring, for communication collaboration and creativity are just a few of the directions considered to define a profession profile. The recommended jobs are described in a modern manner, by allowing the users to explore various WebVR scenarios with specific professional activities. The article provides the technical details of the system, the difficulties of building a stack of such diverse technologies (ML, WebVR, semantic technologies), as well as validation data from experiments with real users: a group of high school students from not so developed cities from Romania, interacting first time with modern technologies.