›› 2018, Vol. 33 ›› Issue (4): 654-667.doi: 10.1007/s11390-018-1847-y

Special Issue: Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Special Section on Recommender Systems with Big Data • Previous Articles     Next Articles

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. 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
  • Received:2018-01-02 Revised:2018-05-29 Online:2018-07-05 Published:2018-07-05
  • 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.
  • Supported by:

    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.

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.

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