Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (10): 2951-2959.DOI: 10.11772/j.issn.1001-9081.2020010086

• Data science and technology • Previous Articles     Next Articles

Course recommendation system based on R2 index and multi-objective differential evolution

HAO Qinxia   

  1. School of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China
  • Received:2020-02-04 Revised:2020-05-18 Online:2020-10-10 Published:2020-05-20
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (51804248), the Education and Teaching Reform and Research Project of Xi'an University of Science and Technology (JG16039).



  1. 西安科技大学 通信与信息工程学院, 西安 710054
  • 通讯作者: 郝秦霞
  • 作者简介:郝秦霞(1980-),女,陕西西安人,副教授,博士研究生,主要研究方向:物联网、数据决策分析。
  • 基金资助:

Abstract: Aiming at the problem of the lack of accurate recommended and selected courses in the new form of higher education, a high-dimensional multi-objective evolutionary algorithm based course guidance and recommendation method was proposed. First, a multi-dimensional fact data warehouse model was designed to save storage space, and the related attributes in the data warehouse such as courses, students, teachers, course difficulty and course recommendation index were formally defined and stipulated. Second, a recommendation model based on high-dimensional R2-MODE (R2 based Multi-Objective Differential Evolution) algorithm was constructed, which improved the search ability in the high-dimensional complex space. Finally, the optimizations of 4 performances, the professionalism of the course teacher, the professional relevance of the course, the degree of the course difficulty and the comprehensive evaluation of the course, were achieved at the same time. Experimental results showed that the proposed algorithm improved the convergence by 50% compared with the reference point-based NSGA-Ⅲ (Third version of Non-dominated Sorting Genetic Algorithm), and had the increase of 5% in the distribution compared with the dominant relationship-based ε-MOEA (ε-dominance based Multi Objective Evolutionary Algorithm). The designed method had the best overall effect on the convergence and distribution of datasets. In the experiment, the accurate recommendation of courses according to the individual characteristics and wishes of students was successfully performed by using the proposed algorithm. The proposed algorithm provided the necessary theoretical support for the accurate guidance and recommendation of course selection on the network platform, and a new method for intelligent course selection.

Key words: fact data warehouse, R2 indicator, high-dimensional multi-objective, multi-objective optimization, course recommendation

摘要: 针对高等教育新形态下网络教学平台缺乏精准推荐选课问题,提出了一种基于高维多目标进化算法的课程引导、推荐式选课方法。首先为节省存储空间设计了多维事实数据仓库模型,并对课程、学生、教师、课程难度、课程推荐指数等数据仓库中的相关属性进行形式化定义以及规约处理;其次构建了基于R2的高维多目标差分进化(R2-MODE)算法的推荐式选课模型,算法改善了高维复杂空间中的搜索能力;最终实现对课程教师专业度、课程的专业相关度、课程难度系数、课程综合评价这4项性能的同时最优化。实验结果表明,所提算法与基于参考点的NSGA-Ⅲ相比,在收敛性上提高了50%,与基于支配关系的ε-MOEA相比,在分布性上提高了5%,所设计的方法在数据集的收敛性和分布性上整体效果最优。实验中,所提算法成功实现了根据学生个体的特征、意愿来进行的课程的精准推荐,为网络平台精准引导、推荐课程选择提供了必要的理论支持,为智能选课提供了一种新的方法。

关键词: 事实数据仓库, R2指标, 高维多目标, 多目标优化, 推荐式选课

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