skip to main content
10.1145/2482991.2482995acmotherconferencesArticle/Chapter ViewAbstractPublication Pagesc-n-tConference Proceedingsconference-collections
research-article

Introducing the space recommender system: how crowd-sourced voting data can enrich urban exploration in the digital era

Published:29 June 2013Publication History

ABSTRACT

Navigation systems like Google Maps and TomTom are designed to generate the shortest and less time consuming path for the user to reach a certain destination from his origin location, not taking into account the user's actual walking experience.

This paper investigates physical and digital urban navigation and describes a new approach by implementing common digital online methods of commenting and recommender systems into the physical world. Those methods are being translated into the urban environment, using Facebook voting data to generate an alternative to the shortest route in order to maximize the pleasure of an urban walk. Initial findings highlight the general importance of the walking experience to the public and suggest that implementing recommendations, based on social media voting systems, in route finding algorithms for mobile applications may enhance the pleasure of urban strolling. The testing of the system in a real world context together with collected feedback and the observations throughout the design process stimulate the discussions of wider issues.

References

  1. Benjamin, W., The Arcades Project, Harvard University Press (2002)Google ScholarGoogle Scholar
  2. Coverley, M., Psychogeography, Pocket Essentials (2010)Google ScholarGoogle Scholar
  3. Feldman, R. Techniques and applications for sentiment analysis, Communications of the ACM, Vol. 56 Issue 4, (2013), 82--89 Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Hiller, B., Penn, A., Hanson, J., Grajewski, T., Xu, J, 1993, Natural movement: or, configuration and attraction in urban pedestrian movement, Environment and Planning B: Planning and Design, 1993, volume 20, pages 29--66, s.n.Google ScholarGoogle ScholarCross RefCross Ref
  5. Kirman, B., "Get Lost, GetLostBot!" Annoying people by offering recommendations when they are not wanted. In Proc LocalPeMA'12, ACM Press (2012), 19.20. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Kirman, B., Linehan, C., Lawson, S., Get lost: facilitating serendipitous exploration in location-sharing services, In Proc CHI'12, ACM Press (2012), 2303--2308. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Kohonen, T., Self-Organizing Maps, 3rd Edition. Springer, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Krüger, A., Aslan, I., Zimmer, H., The effects of mobile pedestrian navigation systems on the concurrent acquisition of route and survey knowledge. Mobile Human-Computer Interaction--MobileHCI 2004, (2004), 39--60.Google ScholarGoogle ScholarCross RefCross Ref
  9. Lindqvist, J., Cranshaw, J., Wiese, J., Hong, H. and Zimmerman, J., I'm the Mayor of My House: Examining Why People Use foursquare, In Proc CHI'11, ACM Press (2011), 2409--2418. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Pariser, E., The Filter Bubble: What the Internet is hiding from you. Penguin Press HC, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Raubal, M., Winter, S., Enriching Wayfinding Instructions with Local Landmarks, In Geographic Information Science -- Proc GIScience 2002, (2002), 243--259. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Ricci, F., Rokach, L., Shapira, B. Introduction to Recommender Systems Handbook, Recommender Systems Handbook, Springer Science+Business Media, LLC (2011)Google ScholarGoogle Scholar
  13. Schöning, J., Hecht, B., Starosielski, N., Evaluating automatically generated location-based stories for tourists, In Proc CHI'08, ACM Press (2008), 2937--2943. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Shang, S., Hui P. Kukami, S. R., Cuff, P. W., 2012, Wisdom of the Crowd: Incorporating Social Influence in Recommendation Models. Proc. ICPADS '11, IEEE (2011), 835--840. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Shepard, M., 2011, Sentient City: ubiquitous computing, architecture, and the future of urban space, The MIT Press, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Traunmueller, M., Fatah gen. Schieck, A., Schöning, J., Brumby, D. P, The Path is the Reward: Considering Social Networks to Contribute to the Pleasure of Urban Strolling, In Proc CHI'13, ACM Press (2013) Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Traunmueller, M. Fatah gen. Schieck, A., Following the Voice of the Crowd: Exploring Opportunities for Using Global Voting Data to Enrich Local Urban Context, CAAD Futures 2013, Springer (2013), CCIS 369, 222--232Google ScholarGoogle Scholar

Index Terms

  1. Introducing the space recommender system: how crowd-sourced voting data can enrich urban exploration in the digital era

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      C&T '13: Proceedings of the 6th International Conference on Communities and Technologies
      June 2013
      165 pages
      ISBN:9781450321044
      DOI:10.1145/2482991

      Copyright © 2013 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 29 June 2013

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      C&T '13 Paper Acceptance Rate17of58submissions,29%Overall Acceptance Rate80of183submissions,44%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader