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A novel recommendation system via L0-regularized convex optimization

  • Deep Learning for Big Data Analytics
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Abstract

In recent decades, a variety of educational management information systems have been presented due to the increase in social requirement globally. Meanwhile, the students in the Universities have also experienced the benefits brought by these platforms for retrieving, acquiring, and leveraging the education resources that might improve their academic performance accordingly. However, most of the previously presented techniques neglected the course recommendation algorithms following the students’ objectives. To bright this gap between the practical requirements and the applications, one convex optimization-based framework with one L0 regularization and the constraint on the learners’ characteristics was presented. To evaluate the proposed method, the comparison experiments were conducted between the state-of-the-art recommendation techniques and ours. Experimental results demonstrated the superior performance of the proposed approach over the previous algorithms especially in accuracy.

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Abbreviations

HR:

Hit rate

ARHR:

Average reciprocal hit-rank

TP:

True positive

FP:

False positive

FN:

False negative

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Acknowledgements

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

Funding

This work was financially supported by the Teaching Reform Research Project of Undergraduate Colleges and Universities of Shandong Province (Z2016Z036), the Teaching Reform Research Project of Shandong University of Finance and Economics (jy2018062891470, jy201830, jy201810), Shandong Provincial Social Science Planning Research Project (18CHLJ08), Scientific Research Projects of Universities in Shandong Province (J18RA136), SDUST Excellent Teaching Team Construction Plan (JXTD20160512), Jinan campus of SDUST Excellent Teaching Team Construction Plan (JNJXTD201711), SDUST Young Teachers Teaching Talent Training Plan (BJRC20160509), Teaching research project of Shandong University of Science and Technology (JNJG2017104) and Scientific and Technological Planning Projects of Universities in Shandong Province (J18KA328).

Author information

Authors and Affiliations

  1. School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, China

    Jinjiao Lin

  2. School of Control Science and Engineering, Shandong University, Jinan, China

    Jinjiao Lin & Yibin Li

  3. Department of Electrical Engineering and Information Technology, Shandong University of Science and Technology, Jinan, China

    Jian Lian

Authors
  1. Jinjiao Lin
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  2. Yibin Li
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  3. Jian Lian
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Contributions

All authors take part in the discussion of the work described in this paper.

Corresponding author

Correspondence to Jian Lian.

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Conflict of interest

These no potential competing interests in our paper. And all authors have seen the manuscript and approved to submit to your journal. We confirm that the content of the manuscript has not been published or submitted for publication elsewhere.

Availability of data and material

We would like very much to share our image dataset with the public upon we get a permission from the hospital where the dataset was acquired. We will try our best to do it because we think it can facilitate the related field’s growth and help on advertising our approach.

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Lin, J., Li, Y. & Lian, J. A novel recommendation system via L0-regularized convex optimization. Neural Comput & Applic 32, 1649–1663 (2020). https://doi.org/10.1007/s00521-019-04213-w

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