? 图书推荐与成绩预测相互增强的联合建模
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Journal of Computer Science and Technology 2018, Vol. 33 Issue (4) :654-667    DOI: 10.1007/s11390-018-1847-y
Special Issue on Software Engineering for High-Confidence Systems << Previous Articles | Next Articles >>
图书推荐与成绩预测相互增强的联合建模
De-Fu Lian1, Member, CCF, ACM, IEEE, Qi Liu2, Member, CCF, IEEE
1 School of Computer Science and Engineering, University of Electronic Science and Technology of China Chengdu 611731, China;
2 School of Computer Science and Technology, University of Science and Technology of China, Hefei 230022, China
Jointly Recommending Library Books and Predicting Academic Performance: A Mutual Reinforcement Perspective
De-Fu Lian1, Member, CCF, ACM, IEEE, Qi Liu2, Member, CCF, IEEE
1 School of Computer Science and Engineering, University of Electronic Science and Technology of China Chengdu 611731, China;
2 School of Computer Science and Technology, University of Science and Technology of China, Hefei 230022, China

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摘要 成绩预测是教育数据挖掘中最重要的任务之一,在慕课和智能教学系统中被广泛研究。学生成绩受个性、技能、社会环境、图书借阅等影响。然而,很少研究图书借阅如何影响学生成绩,更不用说基于图书借阅历史来预测学生成绩。在本文中,我们提出有监督的内容感知的矩阵分解算法来相互增强图书的推荐和成绩的预测。这个算法不仅通过可解释的降维技术来解决稀疏性问题,而且量化了图书在预测成绩的重要性。最后,我们在某大学13,047学生的连续三年图书借阅历史和GPA成绩数据上进行算法评估。实验结果发现我们提出的算法在两个任务上都优于基准算法,而且得出了成绩不仅可以通过借阅图书来预测,而且可以提升图书推荐性能的结论。
关键词教育数据挖掘   学生成绩预测   图书借阅   矩阵分解   多任务学习   迁移学习     
Abstract: The prediction of academic performance is one of the most important tasks in educational data mining, and has been widely studied in massive open online courses (MOOCs) and intelligent tutoring systems. Academic performance can be affected by factors like personality, skills, social environment, and the use of library books. However, it is still less investigated about how the use of library books can affect the academic performance of college students and even leverage book-loan history for predicting academic performance. To this end, we propose a supervised content-aware matrix factorization for mutual reinforcement of academic performance prediction and library book recommendation. This model not only addresses the sparsity challenge by explainable dimension reduction techniques, but also quantifies the importance of library books in predicting academic performance. Finally, we evaluate the proposed model on three consecutive years of book-loan history and cumulative grade point average of 13 047 undergraduate students in one university. The results show that the proposed model outperforms the competing baselines on both tasks, and that academic performance not only is predictable from the book-loan history but also improves the recommendation of library books for students.
Keywordsbook-borrowing record   educational data mining   matrix factorization   multi-task learning   student performance prediction   transfer learning     
Received 2018-01-02;
本文基金:

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61502077 and 61672483, and the Fundamental Research Funds for the Central Universities of China under Grant No. ZYGX2016J087.

About author: De-Fu Lian is an associate professor in the School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu. He received his B.E. and Ph.D. degrees in computer science from University of Science and Technology of China (USTC), Hefei, in 2009 and 2014, respectively. His research interest includes spatial data mining, recommender system, and learning to hash.
引用本文:   
De-Fu Lian, Qi Liu.图书推荐与成绩预测相互增强的联合建模[J]  Journal of Computer Science and Technology , 2018,V33(4): 654-667
De-Fu Lian, Qi Liu.Jointly Recommending Library Books and Predicting Academic Performance: A Mutual Reinforcement Perspective[J]  Journal of Computer Science and Technology, 2018,V33(4): 654-667
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