Acta Scientific Computer Sciences

Mini Review Volume 4 Issue 4

Smart Recommender Systems for Healthcare Domain: Review and Challenges

Anita Shinde and Dipti D Patil*

1Research Scholar, SKNCOE research centre, Savitribai Phule Pune University, Pune, India
2Department of Information Technology. MKSSS’s Cummins College of Engineering for Women, Pune, India

*Corresponding Author: Dipti D Patil, Department of Information Technology. MKSSS’s Cummins College of Engineering for Women, Pune, India.

Received: October 21, 2021; Published: March 03, 2022


Large amount of clinical data spread across various sites on the Internet obstructs users from extracting useful information which can be used for enhancing the healthy life style. Similarly, overload of medical information (e.g. medicines prescriptions, test reports, treatment) has created many problems to medical professionals in building patient oriented decisions. To overcome information overload issue on Internet, Recommendation System has been developed in various fields as a competent tool. There is need to incorporate recommender system in healthcare domain which will be helpful to end-users (patients) and medical professionals in making précised and efficient health related decisions. Now days, the application of recommender system for healthcare has become a critical research topic because of its remarkable benefits in generating relevant personalized recommendations and assisting patients in taking correct health related decisions. In this article, we discuss overview of existing research on recommender systems in healthcare domain. A general idea about three recommendation techniques such as content-based, collaborative filtering (CF)-based, and hybrid methods is explained in context of healthcare domain. Examples of various recommender systems include as food/diet recommendation, drug recommendation, lifestyle recommendation, and healthcare professional recommendation. Finally the research issues need to be addressed are listed.

Keywords: Recommender Systems; Healthcare; Collaborative Filtering; Similarity Measures; Big Data; User Ratings; Decision Support System


  1. W Yue., et al. "An Overview of Recommendation Techniques and Their Applications in Healthcare". in IEEE/CAA Journal of Automatica Sinica4 (2021): 701-717.
  2. Tran TNT., et al. “Recommender systems in the healthcare domain: state-of-the-art and research issues”. Journal of Intelligent Information Systems 57 (2021): 171-201.
  3. Holzinger A., et al. “Towards Interactive Recommender Systems with the Doctor-in-the-Loop”. In: Weyers, B. and Dittmar, A. (Hrsg.), Mensch und Computer Workshop band. Aachen: Gesellschaft für Informatik e.V (2016).
  4. Sahoo AK., et al. “Deep Reco: Deep Learning Based Health Recommender System Using Collaborative Filtering”. Computation 7 (2019): 25.
  5. Jannach D. Recommender systems: an introduction Cambridge University Press.
  6. Lops P., et al. Content-based Recommender systems: State of the Art and Trends (2011): 73-105.
  7. Ricci F., et al. “Recommender systems handbook, 1st edition”. Berlin: Springer (2010).
  8. S´anchez-Bocanegra CL., et al. “Introduction on health recommender systems”. Methods in Molecular Biology 1246 (2015): 131-146.
  9. Sch¨afer H., et al. “Towards health (aware) recommender systems”. In Proceedings of the 2017 International Conference on Digital Health DH 17 (2017): 157-161.
  10. Stettinger M., et al. KNOWLEDGECHECKR: Intelligent techniques for counteracting forgetting (2020).
  11. A Jameson. “A tool that supports the psychologically based design of health-related interventions.” in Proc. 2nd Int. Workshop on Health Recommender Systems Co-Located with the 11th ACM Conf. Recommender Systems, Como, Italy (2017): 39-42.


Citation: Anita Shinde and Dipti D Patil. “Smart Recommender Systems for Healthcare Domain: Review and Challenges". Acta Scientific Computer Sciences 4.4 (2022): 17-19.


Copyright: © 2022 Anita Shinde and Dipti D Patil. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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