Social Interactions over Location-Aware Multimedia Systems

  • Yi YuEmail author
  • Roger Zimmermann
  • Suhua Tang


Advancements in positioning techniques and mobile communications have enabled location-based services with a broad range of location-aware multimedia applications. Accordingly, various social multimedia data, relevant to different aspects of users’ daily life, is aggregated over time on the Internet. Such location-aware multimedia data contains rich context of users and has two implications: individual user interest and geographic-social behaviors. Exploiting these multimedia landscapes helps mine personal preferences, geographic interests and social connections, and brings the opportunities of discovering more interesting topics. In this chapter, we first introduce some examples of location-aware multimedia data and social interaction data. Then, we report some latest methods related to context detection and location-aware multimedia applications. We further present some analysis of geo-social data. Finally, we point out the trend in the integration of social and content delivery networks. In brief, this chapter delivers a picture of emerging geographic-aware multimedia technologies and applications, with location information as a clue.


Social interactions Geo-tagged multimedia Location-aware preference mining Geographic popularity Geo-social behaviors Location recommendations Multimedia content diffusion 



The work presented was in part supported by the Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiative and administered by the IDM Programme Office.


  1. 1.
    Pallis G, Vakali A (2006) Insight and perspectives for content delivery networks. Commun ACM 49(1):101–106CrossRefGoogle Scholar
  2. 2.
    Boyd DM, Ellison NB (2007) Social network sites: definition, history, and scholarship. J Comput-Mediat Commun 13(1):210–230CrossRefGoogle Scholar
  3. 3.
    Junglas IA, Watson RT (2008) Location-based services. Commun ACM 51(3):65–69CrossRefGoogle Scholar
  4. 4.
    Brodersen A, Scellato S, Wattenhofer M (2012) YouTube around the world: geographic popularity of videos. In: WWW’12, pp 241–250Google Scholar
  5. 5.
    Shen ZJ, Arslan Ay S, Kim SH, Zimmermann R (2011) Automatic tag generation and ranking for sensor-rich outdoor videos. In: ACM Multimedia’11, pp 93–102Google Scholar
  6. 6.
    Ma H, Zimmermann R, Kim SH (2012) HUGVid: handling, indexing and querying of uncertain geo-tagged videos. In: ACM SIGSPATIAL’12, pp 319–328Google Scholar
  7. 7.
    Yu Y, Shen, ZJ, Zimmermann R (2012) Automatic music soundtrack generation for outdoor videos from contextual sensor information. In: ACM Multimedia’12, pp 1377–1378Google Scholar
  8. 8.
    Shah RR, Yu Y, Zimmermann R (2014) User preference-aware music video generation based on modeling scene moods. In: ACM MMSys’14, pp 156–159Google Scholar
  9. 9.
    Shah RR, Yu Y, Zimmermann R (2014) ADVISOR-personalized video soundtrack recommendation by late fusion with heuristic rankings. In: ACM Multimedia’14Google Scholar
  10. 10.
    Bao J, Zheng Y, Mokbel MF (2012) Location-based and preference-aware recommendation using sparse geo-social networking data. In: ACM SIGSPATIAL’12, pp 199–208Google Scholar
  11. 11.
    Shimrat M (1962) Algorithm 112: position of point relative to polygon. Commun ACM 5(8):434CrossRefGoogle Scholar
  12. 12.
    Hormann K, Agathos A (2001) The point in polygon problem for arbitrary polygons. Comput Geom 20(3):131–144CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Kupper A, Bareth U, Freese B (2011) Geofencing and background tracking—the next features in LBS. In: INFORMATIK11Google Scholar
  14. 14.
    Yu Y, Tang SH, Zimmermann R (2013) Edge-based locality sensitive hashing for efficient geo-fencing application. In: ACM SIGSPATIAL’13, pp 576–579Google Scholar
  15. 15.
    Qu Y, Zhang J (2013) Trade area analysis using user generated mobile location data. In: WWW’13, pp 1053–1064Google Scholar
  16. 16.
    Quercia D, Di Lorenzo G, Calabrese F, Ratti C (2011) Mobile phones and outdoor advertising: measurable advertising. IEEE Pervasive Comput 10(2):28–36CrossRefGoogle Scholar
  17. 17.
    Scellato S, Noulas A, Lambiotte R, Mascolo C (2011) Socio-spatial properties of online location-based social networks. In: ICWSM’11Google Scholar
  18. 18.
    Scellato S, Mascolo C, Musolesi M, Crowcroft J (2011) Track globally, deliver locally: improving content delivery networks by tracking geographic social cascades. In: WWW’11, pp 457–466Google Scholar
  19. 19.
    Leung D, Newsam S (2012) Exploring geotagged images for land-use classification. In: GeoMM’12, pp 3–8Google Scholar
  20. 20.
    Hauger D, Schedl M (2012) Exploring geospatial music listening patterns in microblog data. In: 10th international workshop on adaptive multimedia retrievalGoogle Scholar
  21. 21.
    Ikawa Y, Vukovic M, Rogstadius J, Murakami A (2013) Location-based insights from the social web. In: WWW’13 companion, pp 1013–1016Google Scholar
  22. 22.
    Eisenstein J, Ahmed A, Xing E (2011) Sparse additive generative models of text. In: ICML’11, pp 1041–1048Google Scholar
  23. 23.
    Hong L, Ahmed A, Gurumurthy S, Smola A, Tsioutsiouliklis K (2012) Discovering geographical topics in the Twitter stream. In: WWW’12, pp 769–778Google Scholar
  24. 24.
    Joachims T, Finley T, Yu CN (2009) Cutting-plane training of structural SVMs. Mach Learn 77(1):27–59CrossRefzbMATHGoogle Scholar
  25. 25.
    Hofmann T (1999) Probabilistic latent semantic indexing. In: ACM SIGIR’99, pp 50–57Google Scholar
  26. 26.
    Kamahara J, Nagamatsu T, Tanaka N (2012) Conjunctive ranking function using geographic distance and image distance for geotagged image retrieval. In: GeoMM’12, pp 9–14Google Scholar
  27. 27.
    Liu Y, Shi Z, Wang G, Guan H (2012) Find you wherever you are: geographic location and environment context-based pedestrian detection. In: GeoMM’12, pp 27–32Google Scholar
  28. 28.
    Robertson S (2004) Understanding inverse document frequency: on theoretical arguments for IDF. J Doc 60(5):503–520CrossRefGoogle Scholar
  29. 29.
    Zheng Y, Zhang L, Xie X, Ma WY (2009) Mining interesting locations and travel sequences from GPS trajectories. In: WWW’09, pp 791–800Google Scholar
  30. 30.
    Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022zbMATHGoogle Scholar
  31. 31.
    Kamath KY, Caverlee J, Lee K, Cheng Z (2013) Spatio-temporal dynamics of online memes: a study of geo-tagged tweets. In: WWW’13, pp 667–678Google Scholar
  32. 32.
    Yamasaki T, Gallagher A, Chen T (2013) Personalized intra- and inter-city travel recommendation using large-scale geotags. In: GeoMM’13, pp 25–30Google Scholar
  33. 33.
    Yu Y, Aizawa K, Yamasaki T, Zimmermann R (2014) Emerging topics on personalized and localized multimedia information systems. In: ACM MM’14Google Scholar
  34. 34.
    Wang J (1999) A survey of web caching schemes for the Internet. ACM SIGCOMM Comput Commun Rev 29(5):36–46CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of ComputingNational University of SingaporeSingaporeSingapore
  2. 2.Graduate School of Informatics and EngineeringThe University of Electro-CommunicationsChofuJapan

Personalised recommendations