Extracting Rankings for Spatial Keyword Queries from GPS Data

  • Ilkcan KelesEmail author
  • Christian S. Jensen
  • Simonas Saltenis
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Studies suggest that many search engine queries have local intent. We consider the evaluation of ranking functions important for such queries. The key challenge is to be able to determine the “best” ranking for a query, as this enables evaluation of the results of ranking functions. We propose a model that synthesizes a ranking of points of interest (PoI) for a given query using historical trips extracted from GPS data. To extract trips, we propose a novel PoI assignment method that makes use of distances and temporal information. We also propose a PageRank-based smoothing method to be able to answer queries for regions that are not covered well by trips. We report experimental results on a large GPS dataset that show that the proposed model is capable of capturing the visits of users to PoIs and of synthesizing rankings.


  1. Alvares LO, Bogorny V, Kuijpers B, de Macedo JAF, Moelans B, Vaisman A (2007) A model for enriching trajectories with semantic geographical information. In: SIGSPATIAL GIS’07, pp 22:1–22:8Google Scholar
  2. Ashbrook D, Starner T (2003) Using GPS to learn significant locations and predict movement across multiple users. Pers Ubiquitous Comput 7(5):275–286CrossRefGoogle Scholar
  3. Baeza-Yates RA, Ribeiro-Neto B (1999) Modern information retrieval. Addison-Wesley Longman Publishing Co, IncGoogle Scholar
  4. Bhattacharya T, Kulik L, Bailey J (2012) Extracting significant places from mobile user GPS trajectories: A bearing change based approach. In: SIGSPATIAL GIS’12, pp 398–401Google Scholar
  5. Bhattacharya T, Kulik L, Bailey J (2015) Automatically recognizing places of interest from unreliable GPS data using spatio-temporal density estimation and line intersections. Pervasive Mob Comput 19:86–107CrossRefGoogle Scholar
  6. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. In: WWW’98, pp 107–117Google Scholar
  7. Cao X, Chen L, Cong G, Jensen CS, Qu Q, Skovsgaard A, Wu D, Yiu ML (2012) Spatial keyword querying. In: ER ’12, pp 16–29Google Scholar
  8. Cao X, Cong G, Jensen CS (2010) Mining significant semantic locations from GPS data. Proc VLDB Endow 3(1–2):1009–1020CrossRefGoogle Scholar
  9. Chen X, Bennett PN, Collins-Thompson K, Horvitz E (2013) Pairwise ranking aggregation in a crowdsourced setting. In: WSDM’13, pp 193–202Google Scholar
  10. Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD’96, pp 226–231Google Scholar
  11. Fagin R, Kumar R, Mahdian M, Sivakumar D, Vee E (2004) Comparing partial rankings. SIAM J Discrete Math 20:47–58Google Scholar
  12. Furletti B, Cintia P, Renso C, Spinsanti L (2013) Inferring human activities from GPS tracks. In: UrbComp’13, pp 5:1–5:8Google Scholar
  13. Google: Understanding consumers’ local search behavior (2014).
  14. Google: Annual search statistics (2016).
  15. Gu Q, Sacharidis D, Mathioudakis M, Wang G (2017) Inferring venue visits from GPS trajectories. In: SIGSPATIAL GIS’17Google Scholar
  16. Hastie T, Tibshirani R, Friedman J (2009) Kernel smoothing methods. Springer, New YorkCrossRefGoogle Scholar
  17. Kang JH, Welbourne W, Stewart B, Borriello G (2004) Extracting places from traces of locations. In: WMASH’04, pp 110–118Google Scholar
  18. Keles I, Saltenis S, Jensen CS (2015) Synthesis of partial rankings of points of interest using crowdsourcing. In: GIR’15, pp 15:1–15:10Google Scholar
  19. Kleinberg JM (1999) Authoritative sources in a hyperlinked environment. J ACM 46(5):604–632.
  20. Kumar R, Mahdian M, Pang B, Tomkins A, Vassilvitskii S (2015) Driven by food: modeling geographic choice. In: WSDM’15, pp 213–222Google Scholar
  21. Montoliu R, Blom J, Gatica-Perez D (2013) Discovering places of interest in everyday life from smartphone data. Multimedia Tools Appl 62(1):179–207CrossRefGoogle Scholar
  22. Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking: bringing order to the web. In: TR 1999-66, Stanford InfoLabGoogle Scholar
  23. Palma AT, Bogorny V, Kuijpers B, Alvares LO (2008) A clustering-based approach for discovering interesting places in trajectories. In: SAC’08, pp 863–868Google Scholar
  24. Shaw B, Shea J, Sinha S, Hogue A (2013) Learning to rank for spatiotemporal search. In: WSDM’13, pp 717–726Google Scholar
  25. Shepard D (1968) A two-dimensional interpolation function for irregularly-spaced data. In: ACM’68, pp 517–524Google Scholar
  26. Spinsanti L, Celli F, Renso C (2010) Where you stop is who you are: Understanding peoples’ activities. In: BMI’10, pp 38–52Google Scholar
  27. Stoyanovich J, Jacob M, Gong X (2015) Analyzing crowd rankings. In: WebDB’15, pp 41–47Google Scholar
  28. Yi J, Jin R, Jain S, Jain A (2013) Inferring users’ preferences from crowdsourced pairwise comparisons: A matrix completion approach. In: HCOMP’13, pp 207–215Google Scholar
  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. Zhou C, Frankowski D, Ludford P, Shekhar S, Terveen L (2004) Discovering personal gazetteers: an interactive clustering approach. In: SIGSPATIAL GIS’04, pp 266–273Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Ilkcan Keles
    • 1
    Email author
  • Christian S. Jensen
    • 1
  • Simonas Saltenis
    • 1
  1. 1.Department of Computer ScienceAalborg UniversityAalborgDenmark

Personalised recommendations