A recommendation model for college majors based on deep learning and clustering algorithms

Q3 Social Sciences
Yu Jian, Ning Xiao, Li Youfeng
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引用次数: 0

Abstract

Many colleges in China have adopted the policy of recruiting students by academic subject categories in order to optimize the talent training mode. To solve the problems in major selection after enrollment, this paper has designed an intelligent algorithm model for recommending college majors. Compared with existing methods for assigning college majors, the model uses deep neural networks and clustering algorithms to simulate complex calculations in the human brain. It uses historical learning data from senior students or graduates to predict the future grades of freshmen, judge their adaptability to various college majors, reduce human interference in the college major selection process, recommend the most suitable college major to students.
基于深度学习和聚类算法的大学专业推荐模型
为了优化人才培养模式,中国的许多高校都采取了按学科分类招生的政策。为了解决学生入学后的专业选择问题,本文设计了一个智能的大学专业推荐算法模型。与现有的大学专业分配方法相比,该模型使用深度神经网络和聚类算法来模拟人脑中的复杂计算。它利用高年级学生或毕业生的历史学习数据来预测大一新生未来的成绩,判断其对各个大学专业的适应能力,减少大学专业选择过程中的人为干扰,为学生推荐最适合的大学专业。
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来源期刊
Information Services and Use
Information Services and Use Social Sciences-Library and Information Sciences
CiteScore
0.90
自引率
0.00%
发文量
41
期刊介绍: Information Services & Use is an information and information technology oriented publication with a wide scope of subject matters. International in terms of both audience and authorship, the journal aims at leaders in information management and applications in an attempt to keep them fully informed of fast-moving developments in fields such as: online systems, offline systems, electronic publishing, library automation, education and training, word processing and telecommunications. These areas are treated not only in general, but also in specific contexts; applications to business and scientific fields are sought so that a balanced view is offered to the reader.
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