基于联合概率矩阵分解的个性化试题推荐方法

doi:10.11772/j.issn.1001-9081.2017082071

计算机应用 ›› 2018, Vol. 38 ›› Issue (3): 639-643.DOI: 10.11772/j.issn.1001-9081.2017082071

基于联合概率矩阵分解的个性化试题推荐方法

李全, 刘兴红, 许新华, 林松

湖北师范大学教育信息与技术学院, 湖北黄石 435002

收稿日期:2017-08-23 修回日期:2017-10-20 出版日期:2018-03-10 发布日期:2018-03-07
通讯作者: 李全
作者简介:李全(1982-),男,湖北黄陂人,讲师,硕士,主要研究方向:机器学习、数据挖掘;刘兴红(1969-),女,湖北蕲春人,教授,硕士,主要研究方向:大数据、教育信息化;许新华(1968-),男,湖北孝感人,教授,硕士,主要研究方向:数据库、数据挖掘;林松(1978-),男,湖北黄石人,讲师,硕士,主要研究方向:搜索引擎、数据挖掘。
基金资助:
湖北省教育科学"十二五"规划项目（2011B130）；国家档案局科技计划项目（2016-x-51）；湖北省高等学校优秀中青年科技创新团队计划项目（T201515）；湖北省教育厅科技项目（D20142504）。

Personalized test question recommendation method based on unified probalilistic matrix factorization

LI Quan, LIU Xinghong, XU Xinhua, LIN Song

Education College of Information Technology, Hubei Normal University, Huangshi Hubei 435002, China

Received:2017-08-23 Revised:2017-10-20 Online:2018-03-10 Published:2018-03-07
Supported by:
This work is partially supported by the Education Science "Twelfth Five-Year" Planning Project of Hubei Province (2011-B130), the Planning Project of Science and Technology of State Archives Bureau (2016-x-51), the Planning Project of Outstanding Young and Middle-aged Scientific and Technological Innovation Team of Universities and Colleges of Hubei Province (T201515), the Science and Technology Project of Education Department of Hubei Province (D20142504).

摘要/Abstract

摘要： 近年来随着在线教育中试题资源数量爆炸式的增长，学生很难在海量的试题资源中找到合适的试题，因此面向学生的试题推荐方法应运而生；然而，传统的基于概率矩阵分解的试题推荐方法没有考虑学生的知识点掌握信息，导致推荐结果准确率低，为此，提出一种基于联合概率矩阵分解的个性化试题推荐方法。首先，通过认知诊断模型得到的学生知识点掌握信息；然后，结合学生、试题和知识点三者信息进行联合概率矩阵分解；最后，根据难度范围进行试题推荐。实验结果表明，与其他传统推荐方法相比，所提方法在不同难度试题推荐的准确率上取得了较好的推荐结果。

关键词: 在线教育, 试题资源, 推荐系统, 联合概率矩阵分解, 认知诊断

Abstract: In recent years, test question resources in online education has grown at an explosive rate. It is difficult for students to find appropriate questions from the mass of question resources. Many test question recommendation methods for students have been proposed to solve this problem. However, many problems exist in traditional test question recommendation methods based on unified probalilistic matrix factorization; especially information of student knowledge points is not considered, resulting in low accuracy of recommendation results. Therefore, a kind of personalized test question recommendation method based on unified probalilistic matrix factorization was proposed. Firstly, through a cognitive diagnosis model, the student knowledge point mastery information was obtained. Secondly, the process of unified probalilistic matrix factorization was executed by combining the information of students, test questions and knowledge points. Finally, according to the difficulty range, the test questions were recommended. The experimental results show that the proposed method gets the best recommedation results in the aspect of accuracy of question recommendation for different range of difficulty, compared to other traditional recommendation methods, and has a good application prospect.

Key words: online education, test question resource, recommendation system, unified probalilistic matrix factorization, cognitive diagnosis

中图分类号:

TP301

李全, 刘兴红, 许新华, 林松. 基于联合概率矩阵分解的个性化试题推荐方法[J]. 计算机应用, 2018, 38(3): 639-643.

LI Quan, LIU Xinghong, XU Xinhua, LIN Song. Personalized test question recommendation method based on unified probalilistic matrix factorization[J]. Journal of Computer Applications, 2018, 38(3): 639-643.

参考文献

[1] VESIN B, IVANOVIC M, BUDIMAC Z, et al. E-learning personalization based on hybrid recommendation strategy and learning style identification[J]. Computers and Education, 2011, 56(3):885-899.
[2] ZHAO W, WU R, LIU H. Paper recommendation based on the knowledge gap between a researcher's background knowledge and research target[J]. Information Processing and Management, 2016, 52(5):976-988.
[3] BOGERS T, van den BOSCH A. Recommending scientific articles using citeulike[C]//Proceedings of the 2008 ACM Conference on Recommender Systems. New York:ACM, 2008:287-290.
[4] TIAN G, JING L. Recommending scientific articles using birelational graph-based iterative RWR[C]//Proceedings of the 20137th ACM Conference on Recommender Systems. New York:ACM, 2013:399-402.
[5] LAI C H, LIU D R, LIN C S. Novel personal and group-based trust models in collaborative filtering for document recommendation[J]. Information Sciences, 2013, 239(8):31-49.
[6] SUN J, MA J, LIU Z, et al. Leveraging content and connection for scientific article recommendation in social computing contexts[J]. The Computer Journal, 2014, 57(9):1331-1342.
[7] CREMONESI P, TURRIN R. Analysis of cold-start recommendations in IPTV system[C]//Proceedings of the 20093rd ACM Conference on Recommender Systems. New York:ACM, 2009:233-236.
[8] GOLUB G, KAHAN K. Calculating the singular values and pseudo-inverse of a matrix[J]. Journal of the Society for Industrial and Applied Mathematics, 1965,2(2):205-224.
[9] LEE D D, SEUNG H S. Algorithms for non-negative matrix factorization[C]//Proceedings of the 200113th International Conference on Neural Information Processing Systems. Cambridge, MA:MIT Press, 2001:535-541.
[10] SALAKHUTDINOV R, MNIH A. Probabilistic matrix factorization[C]//Proceedings of the 200720th International Conference on Neural Information Processing Systems. Vancouver, British Columbia:Curran Associates Inc., 2007:1257-1264.
[11] KOREN Y, BELL R, VOLINSKY C. Matirx factorization for recommender systems[J]. IEEE Computer, 2009, 42(8):30-37.
[12] SOUALI K, AFIA A E, FAIZI R, et al. A new recommender system for e-learning environments[C]//Proceedings of the 2011 International Conference on Multimedia Computing and System. Piscataway:IEEE, 2011:1-4.
[13] 张海东,倪晚成,赵美静,等.面向基础教育阶段的教学资源推荐系统[J].计算机应用,2014,34(11):3353-3356.(ZHANG H D, NI W C, ZHAO M J, et al. Teaching resources recommendation system for K12 education[J]. Journal of Computer Applications, 2014, 34(11):3353-3356.)
[14] WU D, ZHANG G, LIU J. A fuzzy tree matching-based personalised e-learning recommender system[J]. IEEE Transactions on Fuzzy Systems, 2015, 23(6):2412-2426.
[15] DWIVEDI P, BHARADWAJ K K. E-learning recommender system for a group of learners based on the unified learner profile approach[J]. Expert Systems, 2015, 32(2):264-276.
[16] XU W, CAO J, HU L, et al. A social-aware service recommendation approach for mashup creation[C]//Proceedings of the 2013 IEEE 20th Internationnal Conference on Web Services. Washington, DC:IEEE Computer Society, 2013:107-114.
[17] TAMHANE A, IKBAL S, SENGUPTA B, et al. Predicting student risks through longitudinal analysis[C]//Proceedings of the 201420th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM, 2014:1544-1552.
[18] 康春花,孙小坚,顾士伟,等.多水平多维IRT模型在学业质量监测中的应用[J].江西师范大学学报(自然科学版),2016,40(2):133-139.(KANG C H, SUN X J, GU S W, et al. The application of multilevel multidemensional IRT model in academic quality monitoring test[J]. Journal of Jiangxi Normal University (Nature Science Edition), 2016, 40(2):133-139.)
[19] OZAKI K. DINA models for multiple-choice items with few parameters:considering incorrect answers[J]. Applied Psychological Measurement, 2015, 39(6):431-447.
[20] de la TORRE J. The generalized DINA model framework[J]. Psychometrika, 2011, 76(2):179-199.

基于联合概率矩阵分解的个性化试题推荐方法

Personalized test question recommendation method based on unified probalilistic matrix factorization

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