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http://dx.doi.org/10.36498/kbigdt.2022.7.1.187

Non-Curriculum Recommendation Techniques Using Collaborative Filtering for C University  

yujung Janu (충북대학교 빅데이터협동과정)
Kyungeun Yang (충북대학교 경영정보학과)
Wan-Sup Cho (충북대학교 경영정보학과)
Publication Information
The Journal of Bigdata / v.7, no.1, 2022 , pp. 187-192 More about this Journal
Abstract
Many schools are trying to improve students' competencies through many subjects and non-curricular activities, each students has different goals and different activities to prepare for employment. Accordingly, it is difficult to determine whether the programs offered in a comprehensive and comprehensive manner in the existing subject and non-curricular subjects systems are actually suitable for students, so it is necessary to introduce a personalized system. In this study, a method was proposed to classify non-departmental subjects that are uniformly provided to all students of Chungbuk National University by grade level and department. In addition, three types of collaborative filtering models are implemented using the evaluation score of students who participated in the non-curricular program, and personalized recommendations are proposed with the most accurate model by comparing performance.
Keywords
Machine learning; Recommendation system; Classification; Collaborative filtering; KcBERT;
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