课程推荐预测模型优化方案及数据离散化算法  

Optimization Scheme of Course Recommendation Prediction Model andData Discretization Algorithm

作　　者：张戈[1] ZHANG Ge(Department of Computer Teaching and Research,University of Chinese Academy of Social Sciences,Beijing 102488,China)

机构地区：[1]中国社会科学院大学计算机教研部,北京102488

出　　处：《计算机系统应用》2020年第4期248-253,共6页Computer Systems & Applications

基　　金：2020年中国社会科学院大学校级科研项目。

摘　　要：本研究基于k-NN算法建立了课程推荐预测模型.由于原始样本数据的局部不均衡和数据叠交性,预测模型在不进行任何参数调整和数据优化的情况下,模型预测评分并不理想.针对上述问题,本研究设计了一套预测模型参数优化方案和样本数据优化方案,包括最优k值选择算法设计、距离公式优化、数据离散化算法设计.本研究提出的"数据离散化算法"驱使kd树的分类空间排序按照我们期望的特征向量的权重排序,该算法对提升模型预测评分起到了积极作用.上述优化方案和算法设计使课程推荐预测模型的评分从0.67提升到0.85,预测结果的准确度提高了27个百分点,学生对课程推荐的满意度得到显著提升.In this study, the course recommendation prediction model based on k-NN algorithm has been built. Due to the original sample data of the local imbalance and data overlapped, the prediction score of the prediction model is not ideal without any parameter adjustment and data optimization. Aiming at the above problems, this study designed a set of parameter optimization scheme and sample data discretization algorithm of the prediction mode, including the best k value selection algorithm, distance formula optimization, and data discretization algorithm design. In the study, the design of the “data discretization algorithm” drives kd tree classification feature space order sorted by the weight of the characteristic vector that we expect, this algorithm plays a positive role in improving model prediction score. Therefore, all of that increases the grade of the model from 0.67 to 0.85, and the accuracy of prediction results is increased by 27 percentage points, and students' satisfaction with course recommendation is significantly improved.

关 键 词：k-NN算法 最优k值选择 距离公式优化 数据离散化算法 预测模型评分 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]