| 摘 要: IT类专业学生由于其专业特点，企业实习环节往往贯穿整个培养过程，实习环节效果的好坏直接影响到 学生的能力培养与就业质量。如何将实习单位的资源配置、业务特点及学生专长与兴趣等因素进行有机整合，是改善和 提高实习效果的有效途径。本文基于机器学习的方法，对IT专业学生实习单位推荐与评价开展了研究工作，以某高校计 算机专业历年的实习、评价和就业等相关数据为学习样本，自动学习和生成推荐模型与评价体系。实际应用效果表明： 该系统能为实习组织工作提供更加客观的决策支持信息，有效提高学生的实习与就业质量。 |
| 关键词: 推荐系统；机器学习；评价系统 |
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中图分类号: TP181
文献标识码: A
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| A Machine Learning Based Recommendation and Evaluation Method |
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HUANG Xingshou,LIU Di1,2
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1.( 1.College of Mathematics and Statistics, Hechi University, Yizhou 546300, China;2.
2.Computer School of Wuhan University, Wuhan 430072, China)
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| Abstract: The effect of internship experience is a key factor of ability training and employment for college students,especially for IT majors.It is an effective way to improve the practical effect of business internship by integrating the resources and business characteristics of the firms with the expertise and interests of the students.This paper proposes an internship recommendation and evaluation system for IT major students based on machine learning methods by using college's internship and employment data as sample data to generate the recommendation model and the evaluation system.The findings show that the system can provide more objective decision-support information for the organization of internship,and improve students' internship and employment. |
| Keywords: recommended system;machine learning;evaluation system |
