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On the composition and recommendation of multi-feature paths: a comprehensive approach

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Abstract

Trackers have become popular devices these days. They are extensively used to record sports activities (e.g., hiking, skiing), mainly in terms of GPS trajectories, which can be shared on social networking platforms with other users looking for leisure tips. Notably, as the number of available trajectories drastically increased over time, in many cases, it has become challenging, if not impossible, the extensive evaluation of all possible alternatives and the manual selection of the most suitable one. Paths are characterized by multiple features (e.g., dirt, asphalt), and a good representation is needed to satisfy user needs. Moreover, paths can be composed to generate new routes. This calls for a recommender system capable to handle both the multi-feature path representation and the implicit definition of alternatives by composition. This paper suggests a novel approach that features a richer trajectory representation based on a semantic annotation to describe significant path features. Annotations are then used for automatic recommendation of new paths that maximize the presence of characteristics matching the user preferences. Finally, a class of algorithm variants is evaluated using an off-line validation process and compared with a baseline solution to test the underlying assumptions.

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Notes

  1. https://www.wikiloc.com/

  2. http://www.gpsxchange.com/

  3. In this work the well-known (GPX) has been adopted.

  4. The algorithm’s performance, in the original paper, was tested against a dataset containing 3237 vehicle trajectories, for a total of 57109 position samples, generating the underlying map within 11 minutes on a commodity machine.

  5. https://github.com/vcutrona/paths-rs

References

  1. Ahmed M, Wenk C (2012) Constructing street networks from gps trajectories. In: Algorithms—ESA 2012: 20th annual European symposium, 2012. Proceedings. Springer, Berlin, pp 60–71

  2. Audet S, Albertsson C, Murase M, Asahara A (2013) Robust and efficient polygon overlay on parallel stream processors. In: GIS: Proceedings of the ACM international symposium on advances in geographic information systems, pp 304–313

  3. Aung HH, Guo L, Tan KL (2013) Mining sub-trajectory cliques to find frequent routes. In: Advances in spatial and temporal databases: 13th international Symposium, SSTD 2013. Proceedings. Springer, Berlin, pp 92–109

  4. Baraglia R, Frattari C, Muntean CI, Nardini FM, Silvestri F (2012) A trajectory-based recommender system for tourism. In: Active media technology: 8th international conference, AMT 2012. Proceedings. Springer, Berlin, pp 196–205

  5. Cai G, Lee K, Lee I (2018) Itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos. Expert Syst Appl 94:32–40

    Article  Google Scholar 

  6. Cao L, Krumm J (2009) From gps traces to a routable road map. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, GIS ’09 , pp 3–12

  7. Chen C, Lu C, Huang Q, Yang Q, Gunopulos D, Guibas L (2016) City-scale map creation and updating using gps collections. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’16, pp 1465–1474

  8. Choi BCK, Pak AWP (2005) Bias, overview. Encyclopedia of Biostatistics 1:416–423

    Google Scholar 

  9. Cui G, Luo J, Wang X (2018) Personalized travel route recommendation using collaborative filtering based on gps trajectories. Int J Digital Earth 11(3):284–307

    Article  Google Scholar 

  10. Davies JJ, Beresford AR, Hopper A (2006) Scalable, distributed, real-time map generation. IEEE Pervasive Comput 5(4):47–54

    Article  Google Scholar 

  11. Edelkamp S, Schrödl S (2003) Route planning and map inference with global positioning traces. In: Computer science in perspective: essays dedicated to Thomas Ottmann. Springer, Berlin, pp 128–151

  12. Gunawardana A, Shani G (2015) Evaluating recommender systems. In: Recommender systems handbook. Springer US, pp 265–308

  13. Herzog D, Massoud H, Wörndl W (2017) Routeme: a mobile recommender system for personalized, multi-modal route planning. In: Proceedings of the 25th conference on user modeling, adaptation and personalization, UMAP ’17, pp 67–75

  14. Karagiorgou S, Pfoser D (2012) On vehicle tracking data-based road network generation. In: Proceedings of the 20th international conference on advances in geographic information systems, SIGSPATIAL ’12, pp 89–98

  15. Kitayama D, Ozu K, Nakajima S, Sumiya K (2015) A route recommender system based on the user’s visit duration at sightseeing locations. In: Software engineering research, management and applications. Springer International Publishing, pp 177–190

  16. Lee JG, Han J, Whang KY (2007) Trajectory clustering: a partition-and-group framework. In: Proceedings of SIGMOD international conference on management of data, SIGMOD’07, pp 593–604

  17. Liu H, Schneider M (2012) Similarity measurement of moving object trajectories. In: Proceedings of the third ACM SIGSPATIAL international workshop on GeoStreaming, IWGS ’12, pp 19–22

  18. Meratnia N, de By RA (2004) Spatiotemporal compression techniques for moving point objects. In: Advances in database technology - EDBT 2004: 9th international conference on extending database technology. Springer, Berlin, pp 765–782

  19. Merrill D, Garland M, Grimshaw A (2012) Scalable gpu graph traversal. SIGPLAN Not 47(8):117–128

    Article  Google Scholar 

  20. Nardini FM, Orlando S, Perego R, Raffaetà A, Renso C, Silvestri C (2018) Analysing trajectories of mobile users: from data warehouses to recommender systems. Springer International Publishing, pp 407–421

  21. Socharoentum M, Karimi HA (2016) Multi-modal transportation with multi-criteria walking (mmt-mcw): personalized route recommender. Comput Environ Urban Syst 55:44–54

    Article  Google Scholar 

  22. Wang C, Shen Y, Yang H, Guo M (2013) Improving rocchio algorithm for updating user profile in recommender systems. In: Web information systems engineering—WISE 2013: 14th international conference, proceedings, Part I. Springer, Berlin, pp 162–174

  23. Yin P, Ye M, Lee WC, Li Z (2014) Mining gps data for trajectory recommendation. In: Advances in knowledge discovery and data mining: 18th Pacific-Asia conference, PAKDD 2014. Proceedings, Part II. Springer International Publishing, pp 50–61

  24. Ying JC, Chen HS, Lin KW, Lu EHC, Tseng VS, Tsai HW, Cheng KH, Lin SC (2014) Semantic trajectory-based high utility item recommendation system. Expert Syst Appl 41(10):4762–4776

    Article  Google Scholar 

  25. Yoon H, Zheng Y, Xie X, Woo W (2010) Smart itinerary recommendation based on user-generated gps trajectories. In: Ubiquitous intelligence and computing: 7th international conference, UIC 2010. Proceedings. Springer, Berlin, pp 19–34

  26. Zhang Y, Siriaraya P, Wang Y, Wakamiya S, Kawai Y, Jatowt A (2018) Walking down a different path: route recommendation based on visual and facility based diversity. In: Companion proceedings of the the Web conference, WWW ’18, pp 171–174

  27. Zheng Y (2015) Trajectory data mining: an overview. ACM Trans Intell Syst Technol 6(3):29:1–29:41

    Article  Google Scholar 

  28. Zheng Y, Xie X (2011) Learning travel recommendations from user-generated gps traces. ACM Trans Intell Syst Technol 2(1):2:1–2:29

    Article  Google Scholar 

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Authors and Affiliations

  1. University of Milano-Bicocca, Viale Sarca, 336, Milan, Italy

    Vincenzo Cutrona, Federico Bianchi, Michele Ciavotta & Andrea Maurino

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  1. Vincenzo Cutrona
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  2. Federico Bianchi
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  3. Michele Ciavotta
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  4. Andrea Maurino
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Correspondence to Vincenzo Cutrona.

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Cutrona, V., Bianchi, F., Ciavotta, M. et al. On the composition and recommendation of multi-feature paths: a comprehensive approach. Geoinformatica 23, 353–373 (2019). https://doi.org/10.1007/s10707-019-00356-z

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