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

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