Advertisement

Scrutable and Persuasive Push-Notifications

  • Kieran FraserEmail author
  • Bilal Yousuf
  • Owen Conlan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11433)

Abstract

Push-notifications have the potential to reinforce positive behaviours when applied in an intelligent manner. This paper explores a method of improving the delivery process of push-notifications by extracting scrutable persuasive features and refining prediction of notification outcomes. Additionally, a method is proposed for generating recommended notifications, based on the extracted persuasive features, to maximise potential engagement for scenarios such as behavioural interventions. The results illustrate that the persuasive features extracted contributed toward improved push-notification action prediction and that the personalised persuasive notifications recommended vastly increased the Click Through Rate (CTR) of notifications.

Keywords

Push-notifications Synthetic data Scrutable persuasion 

References

  1. 1.
    Cialdini, R.B.: Influence, vol. 3. A. Michel, Port Harcourt (1987)Google Scholar
  2. 2.
    Esteban, C., Hyland, S.L., Rätsch, G.: Real-valued (medical) time series generation with recurrent conditional GANs. arXiv preprint arXiv:1706.02633 (2017)
  3. 3.
    Fraser, K., Yousuf, B., Conlan, O.: Synthesis and evaluation of a mobile notification dataset. In: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization (2017)Google Scholar
  4. 4.
    Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems, pp. 5767–5777 (2017)Google Scholar
  5. 5.
    Thomas, J.R., Masthoff, J., Oren, N.: Personalising healthy eating messages to age, gender and personality: using Cialdini’s principles and framing. In: Proceedings of the 22nd International Conference on Intelligent User Interfaces Companion (2017)Google Scholar
  6. 6.
    Louppe, G., Wehenkel, L., Sutera, A., Geurts, P.: Understanding variable importances in forests of randomized trees. In: Advances in Neural Information Processing Systems, pp. 431–439 (2013)Google Scholar
  7. 7.
    Morrison, L.G., Hargood, C., Pejovic, V., et al.: The effect of timing and frequency of push notifications on usage of a smartphone-based stress management intervention: an exploratory trial. PloS ONE 12(1), e0169162 (2017)CrossRefGoogle Scholar
  8. 8.
    Oulasvirta, A., Rattenbury, T., Ma, L., Raita, E.: Habits make smartphone use more pervasive. Pers. Ubiquit. Comput. 16(1), 105–114 (2012)CrossRefGoogle Scholar
  9. 9.
    Smith, K.A., Dennis, M., Masthoff, J.: Personalizing reminders to personality for melanoma self-checking. In: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, pp. 85–93. ACM (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.ADAPT Centre, School of Computer Science and StatisticsTrinity College DublinDublin 2Ireland

Personalised recommendations