Abstract
Location-based services usually recommend new locations based on the user’s current location or a given destination. However, human mobility involves to a large extent routine behavior and visits to already visited locations. In this paper, we show how daily and weekly routines can be modeled with basic prediction techniques. We compare the methods based on their performance, entropy and correlation measures. Further, we discuss how location prediction for everyday activities can be used for personalization techniques, such as timely or delayed recommendations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Amini, S., Brush, A., Krumm, J., Teevan, J., Karlson, A.: Trajectory-aware mobile search. In: Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems, pp. 2561–2564. ACM (2012)
Ashbrook, D., Starner, T.: Using gps to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing 7, 275–286 (2003)
Bellotti, V., Begole, B., Chi, E., Ducheneaut, N., Fang, J., Isaacs, E., King, T., Newman, M., Partridge, K., Price, B., et al.: Activity-based serendipitous recommendations with the magitti mobile leisure guide. In: Proceedings of the Twenty-Sixth Annual SIGCHI Conference on Human Factors in Computing Systems, pp. 1157–1166. ACM (2008)
Biagioni, J., Krumm, J.: Days of our lives: Assessing day similarity from location traces. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds.) UMAP 2013. LNCS, vol. 7899, pp. 89–101. Springer, Heidelberg (2013)
Brush, A.B., Krumm, J., Scott, J.: Exploring end user preferences for location obfuscation, location-based services, and the value of location. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing, Ubicomp 2010, pp. 95–104. ACM, New York (2010)
Cheng, Z., Caverlee, J., Lee, K.: You are where you tweet: a content-based approach to geo-locating twitter users. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 759–768. ACM, New York (2010)
Cockburn, A., McKenzie, B.J.: What do web users do? an empirical analysis of web use. Int. J. Hum.-Comput. Stud. 54(6), 903–922 (2001)
Etter, V., Kafsi, M., Kazemi, E., Grossglauser, M., Thiran, P.: Where to go from here? mobility prediction from instantaneous information. Pervasive and Mobile Computing 9(6), 784–797 (2013)
Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)
Herder, E., Siehndel, P.: Daily and weekly patterns in human mobility. In: AUM 2012, Workshop on Augmented User Modeling. Extended Proceedings of UMAP 2012 (2012)
Joseph, K., Tan, C.H., Carley, K.M.: Beyond “local”, “categories” and “friends”: clustering foursquare users with latent “topics”. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, UbiComp 2012, pp. 919–926. ACM, New York (2012)
Kawase, R., Papadakis, G., Herder, E., Nejdl, W.: Beyond the usual suspects: context-aware revisitation support. In: Hypertext, pp. 27–36 (2011)
Krumm, J., Brush, A.J.B.: Learning time-based presence probabilities. In: Lyons, K., Hightower, J., Huang, E.M. (eds.) Pervasive 2011. LNCS, vol. 6696, pp. 79–96. Springer, Heidelberg (2011)
Mokbel, M.F., Bao, J., Eldawy, A., Levandoski, J.J., Sarwat, M.: Personalization, socialization, and recommendations in location-based services 2.0. In: Proceedings of the PersDB 2001 Workshop (2011)
Obendorf, H., Weinreich, H., Herder, E., Mayer, M.: Web page revisitation revisited: Implications of a long-term click-stream study of browser usage. In: CHI, pp. 597–606 (2007)
Song, C., Qu, Z., Blumm, N., Barabási, A.L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010)
Tauscher, L., Greenberg, S.: How people revisit web pages: empirical findings and implications for the design of history systems. Int. J. Hum.-Comput. Stud. 47(1), 97–137 (1997)
Teevan, J., Karlson, A., Amini, S., Brush, A.J.B., Krumm, J.: Understanding the importance of location, time, and people in mobile local search behavior. In: Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services, MobileHCI 2011, pp. 77–80. ACM, New York (2011)
Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining interesting locations and travel sequences from gps trajectories. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, pp. 791–800. ACM, New York (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Herder, E., Siehndel, P., Kawase, R. (2014). Predicting User Locations and Trajectories. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, GJ. (eds) User Modeling, Adaptation, and Personalization. UMAP 2014. Lecture Notes in Computer Science, vol 8538. Springer, Cham. https://doi.org/10.1007/978-3-319-08786-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-08786-3_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08785-6
Online ISBN: 978-3-319-08786-3
eBook Packages: Computer ScienceComputer Science (R0)