基于隐语义模型的学生选课推荐算法  

Recommended Algorithm for Students Course-choosing Based on Latent Factor Model

作　　者：陈钢 常笑[1,2,3] 胡枫 CHEN Gang;CHANG Xiao;HU Feng(School of Computer Science,Qinghai Normal University,Xining,Qinghai 810008,China;Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province,Qinghai,Xining 810008,China;Tibetan intelligent information processing and Machine Translation Key Laboratory,Xining,Qinghai 810008,China)

机构地区：[1]青海师范大学计算机学院,青海西宁810008 [2]青海省藏文信息处理与机器翻译重点实验室,青海西宁810008 [3]藏文信息处理教育部重点实验室,青海西宁810008

出　　处：《计算技术与自动化》2021年第3期88-93,共6页Computing Technology and Automation

基　　金：国家自然科学基金资助项目(61663041);青海科技计划资助项目(2018-ZJ-718)。

摘　　要：为了使学生可以准确、合理的进行选修课程,并调动其学习主动性,考虑到学生-课程之间潜在关系,提出了一种基于Funk-SVD技术的隐语义模型学生选课推荐算法。本算法使用随机梯度下降法优化损失函数;对选课推荐算法执行过程中的冷启动问题提出了一种处理方案;通过评价指标召回率、准确率以及平衡F分数验证本算法推荐的可行性和有效性,在所收集到的学生选课数据集上进行测试,实验结果表明,该算法具有一定的优势。In order to enable students to take courses correctly and reasonably,and to arouse their enthusiasm of learning,in view of the actual relationship between students and courses,this thesis proposes a latent factor model of recommended algorithm for students on the basis of Funk-SVD technology.This algorithm applies a method of stochastic gradient descent to optimize the loss function;a solution to solve the problem of cold boot during the process of recommended algorithm for students course-choosing is provide accordingly;the feasibility and validity of this kind of recommended algorithm are verified by evaluating the index recall rate,accuracy rate,and balanced F score,testing on the data collected from students'course-choosing.The experimental results show that the algorithm is advantageous.

关 键 词：推荐算法 潜在关系 隐语义模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]