Skip to main content
SpringerLink
Log in

Where am I? Predicting user location semantics from engagement with smartphone notifications

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Location semantics are important for the delivery of context-aware ubiquitous services to users, such as the contextually-relevant handling of interruptions on mobile devices. For such purposes, user coordinates can be used to query global venue databases, to get back the likely venue (and its categories) where the user is located. This potentially compromises user privacy, allowing service providers to track users. We analyse data from a longitudinal study of 44 participants (university students and staff in Patras, Greece), including notification handling, device state and location information. Using semantic labels from the Google Places API as ground truth, we demonstrate that it is possible to semantically label a user’s location based on their notification handling behaviour, even when location coordinates are obfuscated so as not to precisely match known venue locations. On the other hand, the reliability of this ground truth is questioned through a crowdsourcing exercise. We demonstrate that Places API data can only be reliably used for some venue categories, and recommend best practices for using such data to establish ground truth in location context aware services.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data Availability Statement

The data used in this paper are openly accessible at https://github.com/komis1/ami2019-notifications

References

  • Akosa JS (2017) Predictive accuracy: a misleading performance measure for highly imbalanced data. In: SAS Global Forum 2017 Conference

  • Anderson C, Hübener I, Seipp AK, Ohly S, David K, Pejovic V (2018) A survey of attention management systems in ubiquitous computing environments. ACM Int Mobile Wearable Ubiquitous Technol 2(2):58:1–58:27. https://doi.org/10.1145/3214261

    Article  Google Scholar 

  • Auda J, Weber D, Voit A, Schneegass S (2018) Understanding user preferences towards rule-based notification deferral. In: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, ACM, CHI EA ’18, pp LBW584:1–LBW584:6, https://doi.org/10.1145/3170427.3188688

  • Celik SC, Incel OD (2018) Semantic place prediction from crowd-sensed mobile phone data. J Ambient Intell Humaniz Comput 9(6):2109–2124. https://doi.org/10.1007/s12652-017-0549-6

    Article  Google Scholar 

  • Falcone D, Mascolo C, Comito C, Talia D, Crowcroft J (2014) What is this place? Inferring place categories through user patterns identification in geo-tagged tweets. In: 6th International Conference on Mobile Computing, Applications and Services, pp 10–19, https://doi.org/10.4108/icst.mobicase.2014.257683

  • Gu Y, Yao Y, Liu W, Song J (2016) We know where you are: Home location identification in location-based social networks. In: 2016 25th International Conference on Computer Communication and Networks (ICCCN), pp 1–9, https://doi.org/10.1109/ICCCN.2016.7568598

  • He H, Ma Y (2013) Imbalanced learning: foundations, algorithms, and applications. Wiley, Hoboken

    Book  Google Scholar 

  • He T, Yin H, Chen Z, Zhou X, Sadiq S, Luo B (2016) A spatial-temporal topic model for the semantic annotation of POIs in LBSNs. ACM Trans Intell Syst Technol 8(1):12:1–12:24. https://doi.org/10.1145/2905373

    Article  Google Scholar 

  • Hochmair HH, Juhász L, Cvetojevic S (2018) Data quality of points of interest in selected mapping and social media platforms. In: Kiefer P, Huang H, Van de Weghe N, Raubal M (eds) Progress in Location Based Services 2018. Lecture Notes in Geoinformation and Cartography. Springer International Publishing, Cham, pp 293–313, https://doi.org/10.1007/978-3-319-71470-7_15

  • Huang CM, Ying JJC, Tseng V (2012) Mining users’ behaviors and environments for semantic place prediction. In: Mobile Data Challenge Workshop, citation Key: Huang2012MiningUB

  • Kinsella S, Murdock V, O’Hare N (2011) “i’m eating a sandwich in glasgow”: modeling locations with tweets. In: Proceedings of the 3rd international workshop on Search and mining user-generated contents, Association for Computing Machinery, SMUC ’11, p 61–68, https://doi.org/10.1145/2065023.2065039

  • Komninos A, Frengkou E, Garofalakis J (2018) Predicting user Rrsponsiveness to smartphone notifications for edge computing. In: Kameas A, Stathis K (eds) Ambient Intelligence. Springer International Publishing, Lecture Notes in Computer Science, pp 3–19

  • Komninos A, Simou I, Frengkou E, Garofalakis J (2019) Discovering user location semantics using mobile notification handling behaviour. In: Chatzigiannakis I, De Ruyter B, Mavrommati I (eds) Ambient Intelligence, Springer International Publishing, Lecture Notes in Computer Science, p 219–234, https://doi.org/10.1007/978-3-030-34255-5_15

  • Krumm J, Rouhana D (2013) Placer: semantic place labels from diary data. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM, UbiComp ’13, pp 163–172, https://doi.org/10.1145/2493432.2493504

  • Leppäkoski H, Rivero-Rodriguez A, Rautalin S, Muñoz Martínez D, Käppi J, Ali-Löytty S, Piché R (2017) Semantic labeling of user location context based on phone usage features. Mobile Inf Syst. 10.1155/2017/3876906, https://www.hindawi.com/journals/misy/2017/3876906/

  • Mahmud J, Nichols J, Drews C (2012) Where is this tweet from? inferring home locations of twitter users. In: ICWSM, citation Key: Mahmud2012WhereIT

  • Preoţiuc-Pietro D, Cranshaw J, Yano T (2013) Exploring venue-based city-to-city similarity measures. In: Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, Association for Computing Machinery, New York, NY, USA, UrbComp ’13, https://doi.org/10.1145/2505821.2505832

  • Saikia P, She J (2017) Effective mobile notification recommendation using social nature of locations. In: 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), pp 1265–1270, https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2017.203

  • Visuri A, van Berkel N, Okoshi T, Goncalves J, Kostakos V (2019) Understanding smartphone notifications’ user interactions and content importance. Int J Hum Comput Stud 128:72–85. https://doi.org/10.1016/j.ijhcs.2019.03.001

    Article  Google Scholar 

  • Wu X, Chen L, Lv M, Han M, Chen G (2017) Cost-sensitive semi-supervised personalized semantic place label recognition using multi-context data. ACM Int Mobile Wearable Ubiquitous Technol 1(3):116:1–116:14. https://doi.org/10.1145/3131903

    Article  Google Scholar 

  • Yang D, Li B, Cudré-Mauroux P (2016) Poisketch: semantic place labeling over user activity streams. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, AAAI Press, IJCAI’16, p 2697–2703

  • Ye M, Shou D, Lee WC, Yin P, Janowicz K (2011) On the semantic annotation of places in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, Association for Computing Machinery, KDD ’11, p 520–528, https://doi.org/10.1145/2020408.2020491

  • Zandbergen PA (2009) Accuracy of iPhone locations: a comparison of assisted GPS, WiFi and cellular positioning. Trans GIS 13(s1):5–25. https://doi.org/10.1111/j.1467-9671.2009.01152.x

    Article  Google Scholar 

  • Zhu Y, Zhong E, Lu Z, Yang Q (2013) Feature engineering for semantic place prediction. Pervasive Mobile Comput 9(6):772–783. https://doi.org/10.1016/j.pmcj.2013.07.004

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Patras, 26504, Rio, Greece

    Andreas Komninos & John Garofalakis

  2. Computer Technology Institute and Press “Diophantos”, 26504, Rio, Greece

    Ioulia Simou, Antonis-Elton Frengkou & N. Gkorgkolis

Authors
  1. Andreas Komninos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ioulia Simou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonis-Elton Frengkou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. N. Gkorgkolis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. John Garofalakis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andreas Komninos.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Komninos, A., Simou, I., Frengkou, AE. et al. Where am I? Predicting user location semantics from engagement with smartphone notifications. J Ambient Intell Human Comput 14, 15687–15703 (2023). https://doi.org/10.1007/s12652-020-02680-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-02680-x

Keywords

Search

Navigation