摘要
当前,传统协同过滤的试题推荐仅考虑了用户的做题数据,忽略了试题背后的知识点信息。为此,提出一种基于知识图谱与协同过滤的个性化试题推荐算法(KGeP-CF)。首先,构建知识图谱存储知识点关系。然后,使用TransE模型学习知识点实体的向量表示,使用余弦相似度计算知识点相似度,提取知识点间的相关性,并将其应用于计算试题相似度得到综合试题相似度。最后,结合用户知识点掌握情况推荐用户个性化试题。实验表明,该方法在课程试题推荐上表现出良好的推荐性能,相较于多种推荐算法,KGeP-CF算法推荐性能更优,对其他应用场景也具有一定的参考价值。
At present,the traditional collaborative filtering test recommendation only considers the user’s test data,ignoring the knowledge points behind the test questions.Therefore,a personalized test question recommendation algorithm(KGeP-CF)based on knowledge atlas and collaborative filtering is proposed.First,build a knowledge map to store knowledge point relationships.Then,use TransE model to learn the vector representation of knowledge point entities,use cosine similarity to calculate the similarity of knowledge points,extract the correlation between knowledge points,and apply it to calculate the similarity of test questions to obtain the similarity of comprehensive test questions.Finally,the user personalized test questions are recommended based on the user’s knowledge points.The experiment shows that this method has a good recommendation performance in course test question recommendation.Compared with a variety of recommendation algorithms,KGePCF algorithm has better recommendation performance,and has certain reference value for other application scenarios.
作者
徐硕
尹隽
郭佩瑶
曹伊梦
周杨
方霖
钱萍
XU Shuo;YIN Jun;GUO Pei-yao;CAO Yi-meng;ZHOU Yang;FANG Lin;QIAN Ping(School of Economics and Management,Jiangsu University of Science and Technology;School of Computer Science,Jiangsu University of Science and Technology,Zhenjiang 212100,China)
出处
《软件导刊》
2023年第1期46-51,共6页
Software Guide
基金
国家自然科学基金项目(71972090)
江苏省高等教育教改研究立项课题(2021JSJG227)
江苏科技大学教学改革研究课题(XJG2021020)。
关键词
知识图谱
协同过滤
TransE模型
个性化试题推荐
knowledge graph
collaborative filtering
TransE model
personalized question recommendation
