{"title":"基于深度学习和聚类算法的大学专业推荐模型","authors":"Yu Jian, Ning Xiao, Li Youfeng","doi":"10.3233/isu-230201","DOIUrl":null,"url":null,"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.","PeriodicalId":39698,"journal":{"name":"Information Services and Use","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A recommendation model for college majors based on deep learning and clustering algorithms\",\"authors\":\"Yu Jian, Ning Xiao, Li Youfeng\",\"doi\":\"10.3233/isu-230201\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":39698,\"journal\":{\"name\":\"Information Services and Use\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Services and Use\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/isu-230201\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Services and Use","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/isu-230201","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Social Sciences","Score":null,"Total":0}
A recommendation model for college majors based on deep learning and clustering algorithms
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.
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.