Improving University Programme Recommender System Based on MBTI by Using Gradient Boosted Trees and Firefly Algorithm
Journal Title: International Journal of the Computer, the Internet and Management - Year 2018, Vol 26, Issue 3
Abstract
Choosing a university major after school graduation is a tough question for an undecided student. These students could be qualified students or gifted students. Furthermore, the lack of guidance specialists is one of the various reasons. The framework in this paper aims to encourage students and guidance system with a hybrid machine learning approach such as gradient boosted trees and metaheuristics feature selection with the Myers-Briggs type indicator (MBTI) personality assessment. The main objective of the hybrid recommender system is to identify the pattern of student background, education capability, diverse influences to student, student personality and preferences. Moreover, this paper represents the implementation of a classifier such as gradient boosted trees which are performed well in particular dataset and feature engineering – firefly algorithm was used to improve accuracy and runtime. As a result, the system recommends the appropriate major for each individual need and can use for online guidance in a remote area where at least has a computer with internet access.
Authors and Affiliations
Phuwadol Viroonluecha, Thongchai Kaewkiriya
Keywords
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- EP ID EP601005
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