Recommender System Framework for Academic Choices: Personality Based Recommendation Engine (PBRE)

Abstract

A tremendous growth and progress has shown the potential of big data (i.e structured, unstructured and semi-structured) to extract valuable information and do reliable prediction for several industries. Social networking data has created additional opportunities for data scientists and researchers to utilize the data points to advance the predictive and mining models and techniques. However, predictive analysis in field of academics is at its infancy. In this paper, we present a framework to implement a recommender system to improve academic choice process for new students. The framework is based on our ongoing research for Predicting Educational Relevance For an Efficient Classification of Talent (PERFECT Algorithm Engine), that utilizes stochastic probability distribution based modeling. We present an algorithm and math construct to support our work along with providing graphical results for various parameters that help the recommendation and decision process for individuals. We show related study and conclude with future work.