Adaptive gamification in collaborative location collecting systems: a case of traveling behavior detection




Adaptive gamification challenges, Spatial-temporal user profiling, Users behavioural patterns


Collaborative location collecting systems (CLCS) is a particular case of collaborative systems where a community of users collaboratively collects data associated with a geo-referenced location. Gamification is a strategy to convene participants to CLCS. However, it cannot be generalized because of the different users’ profiles, and so it must be tailored to the users and playing contexts. A strategy for adapting gamification in CLCS is to build game challenges tailored to the player’s spatio-temporal behavior. This type of adaptation requires having a user traveling behavior profile. Particularly, this work is focused on the first steps to detect users’ behavioral profiles related to spatial-temporal activities in the context of CLCS. Specifically, this article introduces: (1) a strategy to detect patterns of spatial-temporal activities, (2) a model to describe the spatial-temporal behavior of users based on (1), and a strategy to detect users’ behavioral patterns based on unsupervised clustering.  The approach is evaluated over a Foursquare dataset. The results showed two types of behavioral atoms and two  types of users’ behavioral patterns. 


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A. Noulas, S. Scellato, C. Mascolo, and M. Pontil, “An empirical study of geographic user activity patterns in foursquare,” Proceedings of the International AAAI Conference on Web and Social Media, vol. 5, Jul. 2011.

J. Lindqvist, J. Cranshaw, J. Wiese, J. Hong, and J. Zimmerman, “I’m the mayor of my house: Examining why people use foursquare - a social-driven location sharing application,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’11, (New York, NY, USA), p. 2409–2418, Association for Computing Machinery, 2011.

G. Van Horn, O. Mac Aodha, Y. Song, Y. Cui, C. Sun, A. Shepard, H. Adam, P. Perona, and S. Belongie, “The inaturalist species classification and detection dataset,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.

M. D. Ayastuy, D. Torres, and A. Fernández, “Adaptive gamification in Collaborative systems, a systematic mapping study,” Computer Science Review, vol. 39, p. 100333, 2021.

S. Deterding, D. Dixon, R. Khaled, and L. Nacke, “From Game Design Elements to Gamefulness: Defining ”Gamification”,” in Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments, MindTrek ’11, (New York, NY, USA), pp. 9–15, ACM, 2011. event-place: Tampere, Finland.

M. Böckle, J. Novak, and M. Bick, “Towards adaptive gamification: a synthesis of current developments,” in Proceedings of the 25th European Conference on Information Systems (ECIS), (Guimarães, Portugal), 2017.

S. Göbel and V. Wendel, “Personalization and adaptation,” in Serious Games, pp. 161–210, Springer, 2016.

S. Iversen, “In the double grip of the game: Challenge and Fallout 3,” Game Studies, vol. 12, 2012.

J. Vahlo and V.-M. Karhulahti, “Challenge types in gaming validation of video game challenge inventory (CHA),” International Journal of Human-Computer Studies, vol. 143, p. 102473, 2020.

L. Ponciano and T. E. Pereira, “Characterising volunteers’ task execution patterns across projects on multi-project citizen science platforms,” in Proceedings of the 18th Brazilian Symposium on Human Factors in Computing Systems, IHC ’19, (New York, NY, USA), Association for Computing Machinery, 2019.

M. Aristeidou, E. Scanlon, and M. Sharples, “Profiles of engagement in online communities of citizen science participation,” Computers in Human Behavior, vol. 74, pp. 246–256, 2017.

D. Yang, D. Zhang, V. W. Zheng, and Z. Yu, “Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 1, pp. 129–142, 2015.

X. Long, L. Jin, and J. Joshi, “Exploring trajectory-driven local geographic topics infoursquare,” in Proceedings of the 2012 ACM conference on ubiquitous computing, pp. 927–934, 09 2012.

R. Tibshirani, G. Walther, and T. Hastie, “Estimating the number of clusters in a data set via the gap statistic,” Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 63, no. 2, pp. 411–423, 2001.

H. A. Abu Alfeilat, A. B. Hassanat, O. Lasassmeh, A. S. Tarawneh, M. B. Alhasanat, H. S. Eyal Salman, and V. S. Prasath, “Effects of distance measure choice on k-nearest neighbor classifier performance: A review,” Big Data, vol. 7, no. 4, pp. 221–248, 2019. PMID: 31411491.

F. Petitjean, A. Ketterlin, and P. Gançarski, “A global averaging method for dynamic time warping, with applications to clustering,” Pattern Recognition, vol. 44, no. 3, pp. 678–693, 2011.




How to Cite

Dalponte Ayastuy, M., & Torres, D. (2022). Adaptive gamification in collaborative location collecting systems: a case of traveling behavior detection. Journal of Computer Science and Technology, 22(1), e05.



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