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Volume 8 Issue 4
Apr.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Wenbin Yue, Zidong Wang, Jieyu Zhang and Xiaohui Liu, "An Overview of Recommendation Techniques and Their Applications in Healthcare," IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 701-717, Apr. 2021. doi: 10.1109/JAS.2021.1003919
Citation: Wenbin Yue, Zidong Wang, Jieyu Zhang and Xiaohui Liu, "An Overview of Recommendation Techniques and Their Applications in Healthcare," IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 701-717, Apr. 2021. doi: 10.1109/JAS.2021.1003919

An Overview of Recommendation Techniques and Their Applications in Healthcare

doi: 10.1109/JAS.2021.1003919
Funds:  This work was supported in part by the National Natural Science Foundation of China (61873148, 61933007), the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
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  • With the increasing amount of information on the internet, recommendation system (RS) has been utilized in a variety of fields as an efficient tool to overcome information overload. In recent years, the application of RS for health has become a growing research topic due to its tremendous advantages in providing appropriate recommendations and helping people make the right decisions relating to their health. This paper aims at presenting a comprehensive review of typical recommendation techniques and their applications in the field of healthcare. More concretely, an overview is provided on three famous recommendation techniques, namely, content-based, collaborative filtering (CF)-based, and hybrid methods. Next, we provide a snapshot of five application scenarios about health RS, which are dietary recommendation, lifestyle recommendation, training recommendation, decision-making for patients and physicians, and disease-related prediction. Finally, some key challenges are given with clear justifications to this new and booming field.

     

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    • A comprehensive review of typical recommendation techniques is presented.
    • Healthcare applications of recommendation techniques are discussed.
    • Recent progresses on recommendation systems are categorized.
    • Key challenges for recommendation techniques are highlighted.
    • Future research directions for recommendation systems are pointed out.

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