Authors:
Jaeheon Park
1
;
Suan Lee
2
;
Woncheol Lee
3
and
Jinho Kim
4
Affiliations:
1
Kangwon Joint Program of Software Convergence Course, Kangwon National University, Chuncheon, South Korea
;
2
School of Computer Science, Semyung University, Jecheon, South Korea
;
3
SeedsSoft Co., Ltd., Chuncheon, South Korea
;
4
Dept. of Computer Science and Engineering, Kangwon National University, Chuncheon, South Korea
Keyword(s):
Course Recommendation, Collaborative Filtering, Hybrid Model, Deep Learning.
Abstract:
This study introduces a novel recommendation system aimed at enhancing university career counseling by adapting it to more accurately align with students’ interests and career trajectories. Recognizing the challenges students face in selecting courses that complement their career goals, our research explores the efficacy of employing both collaborative filtering and a hybrid model approach in the development of this system. Uniquely, this system utilizes a company-course recommendation method, diverging from the traditional student-course paradigm, to generalize company-course relationships, thereby enhancing the system’s recommendation precision. Through meticulous feature engineering, we improved the performance of the NeuMF model. Our experiments demonstrate that the proposed method outperforms other models by 10% to 79% based on the mAP metric, suggesting that the proposed model can effectively recommend courses for employment.