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Learning to recognise disruptive smartphone notifications

Published:23 September 2014Publication History

ABSTRACT

Short term studies in controlled environments have shown that user behaviour is consistent enough to predict disruptive smartphone notifications. However, in practice, user behaviour changes over time (concept drift) and individual user preferences need to be considered. There is a lack of research on which methods are best suited for predicting disruptive smartphone notifications longer-term, taking into account varying error costs. In this paper we report on a 16 week field study comparing how well different learners perform at mitigating disruptive incoming phone calls.

References

  1. Avrahami, D., and Hudson, S. E. Responsiveness in instant messaging: Predictive models supporting inter-personal communication. In CHI (2006). Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Fischer, J. E., Greenhalgh, C., and Benford, S. Investigating episodes of mobile phone activity as indicators of opportune moments to deliver notifications. In Proc. of MobileHCI '11, ACM (2011), 181--190. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Fisher, R., and Simmons, R. Smartphone interruptibility using density-weighted uncertainty sampling with reinforcement learning. In Proc. of ICMLA '11 - Volume 01, IEEE (2011), 436--441. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Hincapié-Ramos, J. D., Voida, S., and Mark, G. A design space analysis of availability-sharing systems. In Proc. of UIST '11, ACM (2011), 85--96. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Ho, J., and Intille, S. S. Using context-aware computing to reduce the perceived burden of interruptions from mobile devices. In Proc. of CHI '05, ACM (2005), 909--918. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Hudson, S. E., Fogarty, J., Atkeson, C. G., Avrahami, D., Forlizzi, J., Kiesler, S. B., Lee, J. C., and Yang, J. Predicting human interruptibility with sensors: a wizard of oz feasibility study. In CHI, ACM (2003), 257--264. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Kern, N., and Schiele, B. Towards personalized mobile interruptibility estimation. In Proc. of LoCA'06, Springer (2006), 134--150. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Khalil, A., and Connelly, K. Improving cell phone awareness by using calendar information. In Proc. of INTERACT'05, Springer-Verlag (2005), 588--600. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Koza, J. R., Bennett III, F. H., and Stiffelman, O. Genetic programming as a Darwinian invention machine. Springer, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Lazarescu, M., Venkatesh, S., and Bui, H. H. Using multiple windows to track concept drift. Intell. Data Anal. 8, 1 (2004), 29--59. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Leiva, L. A., Böhmer, M., Gehring, S., and Krüger, A. Back to the app: the costs of mobile application interruptions. In Mobile HCI (2012), 291--294. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Liu, B., Hsu, W., and Ma, Y. Integrating classification and association rule mining. In KDD (1998), 80--86.Google ScholarGoogle Scholar
  13. Markitanis, A. Learning mobile user behaviours. Master's thesis, Imperial College London, 2011.Google ScholarGoogle Scholar
  14. Markitanis, A., Corapi, D., Russo, A., and Lupu, E. C. Learning user behaviours in real mobile domains. In Proc. of ILP'11 (2011).Google ScholarGoogle Scholar
  15. Pielot, M., de Oliveira, R., Kwak, H., and Oliver, N. Didn't you see my message?: Predicting attentiveness to mobile instant messages. In CHI (2014). Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Rosenthal, S., Dey, A. K., and Veloso, M. Using decision-theoretic experience sampling to build personalized mobile phone interruption models. In Proc. of Pervasive'11, Springer-Verlag (2011), 170--187. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Smith, J., and Dulay, N. Ringlearn: Long-term mitigation of disruptive smartphone interruptions. In ACOMORE, PerCom '14 (2014).Google ScholarGoogle ScholarCross RefCross Ref
  18. ter Hofte, G. H. H. Xensible interruptions from your mobile phone. In Proc. of MobileHCI '07, ACM (2007), 178--181. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Learning to recognise disruptive smartphone notifications

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    • Published in

      cover image ACM Conferences
      MobileHCI '14: Proceedings of the 16th international conference on Human-computer interaction with mobile devices & services
      September 2014
      664 pages
      ISBN:9781450330046
      DOI:10.1145/2628363

      Copyright © 2014 ACM

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      New York, NY, United States

      Publication History

      • Published: 23 September 2014

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      MobileHCI '14 Paper Acceptance Rate35of124submissions,28%Overall Acceptance Rate202of906submissions,22%

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