Designing Recommender System for Corporate Education WiP+32

In order to survive in a competitive environment, getting placed in a reputed company after graduation is not the end of education. Within a company also a person needs to keep re-skilling and up-skilling oneself to stay relevant and successful. A Recommender system would help new employees to identify the best choice (course) of their interest. In this paper, we have proposed a recommender system that would assist new employees by predicting for them the choices of certifications and skills that they should acquire to advance in their careers, given their personal histories (education and already acquired skills and certifications). We have employed a relatively new technique, Compact Prediction Trees, for this task. CPTs have shown convincing results in sequence prediction, performing better and faster than many traditional algorithms.