A Personalized Health Recommendation System Based on Smartphone Calendar Events

  • Sharvil KatariyaEmail author
  • Joy Bose
  • Mopuru Vinod Reddy
  • Amritansh Sharma
  • Shambhu Tappashetty
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10898)


Many e-health services are available to users today, but they often suffer from lack of personalization. In this paper, we present a system to generate personalized health recommendations from various providers, based on classification of health related calendar events on the user’s smartphone. Due to privacy constraints, such personal data often cannot be uploaded to external servers, hence the classification and personalization has to run on the client device. We use a server to train our model to classify calendar events using SVM and fastText, while the prediction is run on the client device using the trained model. The class labels from the classified calendar events, weighted in order of recency, are used to build a vector, which we treat as a representation of user interest while personalizing the recommendations. This vector is used to re-rank health related recommendations obtained from third party providers based on relevance. We describe the implementation details of our system and some tests on its accuracy and relevance to provide relevant health related recommendations. While we used the calendar app to classify events, our system can also be extended for other apps such as messaging.


Health recommendation system e-Health Calendar Classifier FastText SVM 


  1. 1.
    Eysenbach, G.: What is e-health? J. Med. Internet Res. 3(2), e20 (2001). Scholar
  2. 2.
    Germanakos, P., Mourlas, C., Samaras, G.: A mobile agent approach for ubiquitous and personalized eHealth information systems. In: Proceedings of the Workshop on ‘Personalization for e-Health’of the 10th International Conference on User Modeling (UM 2005), pp. 67–70, 29 July 2005Google Scholar
  3. 3.
    Liu, C., Zhu, Q., Holroyd, K.A., Seng, E.K.: Status and trends of mobile-health applications for iOS devices: a developer’s perspective. J. Syst. Softw. 84(11), 2022–2033 (2011)CrossRefGoogle Scholar
  4. 4.
    Free, C., Phillips, G., Galli, L., Watson, L., Felix, L., Edwards, P., Patel, V., Haines, A.: The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review. PLoS Med. 10(1), e1001362 (2013)CrossRefGoogle Scholar
  5. 5.
    Abroms, L.C., Ahuja, M., Kodl, Y., Thaweethai, L., Sims, J., Winickoff, J.P., Windsor, R.A.: Text2Quit: results from a pilot test of a personalized, interactive mobile health smoking cessation program. J. Health Commun. 17(sup1), 44–53 (2012)CrossRefGoogle Scholar
  6. 6.
    Beratarrechea, A., Lee, A.G., Willner, J.M., Jahangir, E., Ciapponi, A., Rubinstein, A.: The impact of mobile health interventions on chronic disease outcomes in developing countries: a systematic review. Telemed. e-Health 20(1), 75–82 (2014)CrossRefGoogle Scholar
  7. 7.
    Lorenz, A., Mielke, D., Oppermann, R., Zahl, L.: Personalized mobile health monitoring for elderly. In: Proceedings of the 9th International Conference on Human Computer Interaction with Mobile Devices and Services pp. 297–304. ACM, 9 September 2007Google Scholar
  8. 8.
    Chomutare, T., Fernandez-Luque, L., Årsand, E., Hartvigsen, G.: Features of mobile diabetes applications: review of the literature and analysis of current applications compared against evidence-based guidelines. J. Med. Internet Res. 13(3) (2011)CrossRefGoogle Scholar
  9. 9.
    Ricci, F.: Mobile recommender systems. Information Technology & Tourism. 12(3), 205–231 (2010)CrossRefGoogle Scholar
  10. 10.
    Share your calendar with someone - Calendar Help - Google Support.
  11. 11.
    Holy name Medical Center: Calendar of Events.
  12. 12.
    Archive Team: The Twitter Stream Grab.
  13. 13.
  14. 14.
    Chang, C.C.: LIBSVM – A Library for Support Vector Machines.
  15. 15.
    Github. libsvm/java at master · cjlin1/libsvm · GitHub.
  16. 16.
    GitHub - Java port of c++ version of facebook fasttext.
  17. 17.
  18. 18.
    Jannach, D. and Lerche, L., Gdaniec, M.: Re-ranking recommendations based on predicted short-term interests – a protocol and first experiment. In: ITWP 2013: Proceedings of the Workshop Intelligent Techniques for Web Personalization and Recommender Systems at AAAI (2013)Google Scholar
  19. 19.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI 2009)Google Scholar
  20. 20.
    Wikipedia: Spearman’s rank correlation coefficient.

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Sharvil Katariya
    • 1
    Email author
  • Joy Bose
    • 1
  • Mopuru Vinod Reddy
    • 1
  • Amritansh Sharma
    • 1
  • Shambhu Tappashetty
    • 1
  1. 1.Samsung R&D InstituteBangaloreIndia

Personalised recommendations