基于隐语义模型的学生选课推荐算法
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引用本文:陈 钢1,2,3,常 笑1,2,3,胡 枫1,2,3.基于隐语义模型的学生选课推荐算法[J].计算技术与自动化,2021,(3):88-93
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作者单位
陈 钢1,2,3,常 笑1,2,3,胡 枫1,2,3 (1.青海师范大学 计算机学院, 青海 西宁 8100082.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) 
中文摘要: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。
中文关键词:推荐算法  潜在关系  隐语义模型
 
Recommended Algorithm for Students Course-choosing Based on Latent Factor Model
Abstract: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.
keywords:recommended algorithm  actual relationship  latent factor model
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