基于LGB-FFM-LR算法的在线课程评分预测方法研究  

Prediction methods of online course grading based on LGB-FFM-LR

作　　者：刘昱萌 刘斌[1] Liu Yumeng;Liu Bin(School of Electronic Information and Artificial Intelligence,Shaanxi University of Science&Technology,Xi'an 710021,China)

机构地区：[1]陕西科技大学电子信息与人工智能学院,西安710021

出　　处：《电子测量技术》2021年第16期1-6,共6页Electronic Measurement Technology

基　　金：国家自然科学基金(61871260)项目资助。

摘　　要：针对在线教育课程客观评价较差的问题,设计了基于决策树算法的梯度提升算法-场感知因式分解机-逻辑回归(LightGBM-FFM-LR)算法的评分预测模型。该模型采集在线课程观看历史数据,提取用户的通用特征、时间特征等特征值,并着重考虑特征值的高维特征和低维特征关系来实现多维特征组合,改善数据稀疏性,从而提升评分预测性能。通过对某在线课程网站的脱敏数据实验表明,该模型的评分预测值与评分实际值的决定系数为0.87,平均均方误差为0.42,提升了模型的泛化能力,对在线课程的预测评分结果更加客观真实。For the problem of poor objective evaluation of online education courses,a prediction model based on light gradient boosting machine-field-aware factorization machine-logistic regression is designed.The model collects online course viewing history data,extracts users′generic features,temporal features and other feature values,and focuses on the relationship between high-dimensional features and low-dimensional features of feature values to achieve multi-dimensional feature combinations and improve data sparsity,so as to improve rating prediction performance.Based on masked data test on an online course website,the determination coefficient between predicted grading and actual one in this model is 0.87,with 0.42 of average mean square error,improved model generalization capability,this model serves more objective and realistic predicted grading to online course.

关 键 词：在线课程 评分预测 LightGBM FFM LR 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]