Skip to main content

Urban Context Detection and Context-Aware Recommendation via Networks of Humans as Sensors

  • Conference paper
Agent Technology for Intelligent Mobile Services and Smart Societies (AVSA 2014, CARE 2014)

Abstract

The wide adoption of smart mobile devices makes the concept of human as a sensor possible, opening the door to new ways of solving recurrent problems that occur in everyday life by taking advantage of the information these devices can produce. In the case of this paper, we present part of the work done in the EU project SUPERHUB and introduce how geolocated positioning coming from such devices can be used to infer the current context of the city, e.g., disruptive events, and how this information can be used to provide services to the end-users.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M.,, D.: HWANG. Complex networks: Structure and dynamics. Physics Reports 424(4-5), 175–308 (2006)

    Google Scholar 

  2. Carreras, I., Gabrielli, S., Miorandi, D., Tamilin, A., Cartolano, F., Jakob, M., Marzorati, S.: SUPERHUB: A user-centric perspective on sustainable urban mobility. In: Sense Transport 2012: Proc. of the 6th ACM Workshop on Next Generation Mobile Computing for Dynamic Personalised Travel Planning. ACM (June 2012)

    Google Scholar 

  3. Codina, V., Ricci, F., Ceccaroni, L.: Local Context Modeling with Semantic Pre-filtering. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 363–366. ACM, New York (2013)

    Chapter  Google Scholar 

  4. Cretti, S., Facca, F.: D2.1 FP7-ICT-2011-7 SUPERHUB - Report on the architecture definition Open Source strategy and adoption pattern. Technical report (March 2012)

    Google Scholar 

  5. Diaspero, C., Heinisch, A., Petrova, A.: A Mobile Recommender System for Hiking Walkways (2011)

    Google Scholar 

  6. Ellul, C., Gupta, S., Haklay, M.M., Bryson, K.: A Platform for Location Based App Development for Citizen Science and Community Mapping. In: Progress in Location-Based Services, pp. 71–90. Springer, Heidelberg (2013)

    Google Scholar 

  7. Gabrielli, L., Rinzivillo, S., Ronzano, F., Villatoro, D.: From Tweets to Semantic Trajectories: Mining Anomalous Urban Mobility Patterns. In: Nin, J., Villatoro, D. (eds.) CitiSens 2013. LNCS (LNAI), vol. 8313, pp. 26–35. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  8. Garcia-Gasulla, D., Tejeda-Gómez, A., Alvarez-Napagao, S., Oliva-Felipe, L., Vázquez-Salceda, J.: Detection of events through collaborative social network data. In: Proceedings of the 6th International Workshop on Emergent Intelligence on Networked Agents (WEIN 2014) (May 2014)

    Google Scholar 

  9. Gomez, J.T., Marrè, M.S., Serra, J.P.: tweetStimuli: Discovering social structure of influence (2012)

    Google Scholar 

  10. Hickey, R.: The Clojure programming language. In: DLS 2008: Proceedings of the 2008 Symposium on Dynamic Languages. ACM (July 2008)

    Google Scholar 

  11. Knoch, S., Chapko, A., Emrich, A., Werth, D., Loos, P.: A Context-Aware Running Route Recommender Learning from User Histories Using Artificial Neural Networks. In: 2012 23rd International Workshop on Database and Expert Systems Applications (DEXA), pp. 106–110 (2012)

    Google Scholar 

  12. McGinty, L., Smyth, B.: Personalised Route Planning: A Case-Based Approach. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS (LNAI), vol. 1898, pp. 431–443. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  13. Ricci, F.: Travel recommender systems. IEEE Intelligent Systems (2002)

    Google Scholar 

  14. Srivastava, M., Abdelzaher, T., Szymanski, B.: Human-centric sensing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370, 176–197 (1958, 2011)

    Google Scholar 

  15. Weng, J., Lee, B.S.: Event Detection in Twitter. In: ICWSM (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Alvarez-Napagao, S. et al. (2015). Urban Context Detection and Context-Aware Recommendation via Networks of Humans as Sensors . In: Koch, F., Meneguzzi, F., Lakkaraju, K. (eds) Agent Technology for Intelligent Mobile Services and Smart Societies. AVSA CARE 2014 2014. Communications in Computer and Information Science, vol 498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46241-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-46241-6_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-46240-9

  • Online ISBN: 978-3-662-46241-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics