Abstract
With the proliferation of mobile devices and the huge variety of sensors they incorporate, it is possible to register the user location on the move. Based on historical records, it is feasible to predict user location in space or space and time. Studies show that user mobility patterns have a high degree of repetition and this regularity has been exploited to forecast the next location of the user. Furthermore, proposals have been made to forecast user location in space and time; in particular, we present a spatio-temporal prediction model that we developed to forecast user location in a medium-term with good accuracy results. After explaining how collaborative filtering (CF) works, we explore the feasibility of using collective preferences to avoid missing POIs and therefore increase the prediction accuracy. To test the performance of the method based on CF, we compare our spatio-temporal prediction model with and without using the method based on CF.
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References
Gonzalez MC, Hidalgo CA, Barabasi A-L (2008) Understanding individual human mobility patterns. Nature 453(7196):779–782
Scellato S, Musolesi M, Mascolo C, Latora V, Campbell AT (2011) NextPlace: a spatiotemporal prediction framework for pervasive systems. In: Proceedings of the 9th international conference on pervasive computing, Pervasive ’11, Springer, pp 152–169
Sadilek A, Krumm J (2012) Far out: predicting long-term human mobility. In: Proceedings of the 26th AAAI conference on Artificial Intelligence, AAAI, AAAI Press
Alvarez-Lozano J, García-Macías JA, Chávez E (2015) Crowd location forecasting at points of interest. Int J Ad Hoc Ubiquitous Comput 18(4):191–204
Schafer, J. B., Konstan, J. & Riedl, J. (1999), Recommender systems in e-commerce. In: Proceedings of the 1st ACM conference on Electronic Commerce, EC ’99, ACM, New York, NY, USA, pp 158–166
Eagle N, Pentland A (2006) Reality mining: sensing complex social systems. Pers Ubiquit Comput 10(4):255–268
Farrahi K, Gatica-Perez D (2011) Discovering routines from large-scale human locations using probabilistic topic models. ACM Trans Intell. Syst Technol 2(1):3:1–3:27
Motahari S, Zang H, Reuther P (2012) The impact of temporal factors on mobility patterns. In: Proceedings of the 2012 45th Hawaii International Conference on System Sciences, HICSS ’12, IEEE Computer Society, pp 5659–5668
Yavas G, Katsaros D, Ulusoy O, Manolopoulos Y (2004) A data mining approach for location prediction in mobile environments. Data & Knowl Eng 54(2005):121–146
Chon Y, Shin H, Talipov E, Cha H (2012) Evaluating mobility models for temporal prediction with high-granularity mobility data. In: Proceedings of the 2012 IEEE international conference on Pervasive Computing and Communications, Percom, IEEE, pp 206–212
Hsu W, Spyropoulos T, Psounis K, Helmy A (2007) Modeling time-variant user mobility in wireless mobile networks. In: Proceedings of the 26th IEEE international conference on computer communications, INFOCOM, IEEE, Anchorage, Alaska, USA, pp 758–766
Markov AA (1961) Theory of algorithms, Israel program for scientific translations. Bloomington, IN, USA
Do TMT, Gatica-Perez D (2012) Contextual conditional models for smartphone-based human mobility prediction. In: Proceedings of the 2012 ACM conference on Ubiquitous Computing, UbiComp ’12, ACM, pp 163–172
Viterbi AJ (2006) A personal history of the Viterbi algorithm. IEEE Signal Process Mag 23(4):120–142
Ashbrook D, Starner T (2003) Using gps to learn significant locations and predict movement across multiple users. Pers Ubiquit Comput 7(5):275–286
Kang JH, Welbourne W, Stewart B, Borriello G (2005) Extracting places from traces of locations. SIGMOBILE Mob Comput Commun Rev 9(3):58–68
Kim M, Kotz D, Kim S (2006) Extracting a mobility model from real user traces. In: Proceedings of the 25th IEEE international conference on computer communications, INFOCOM ’06, pp 1–13
Marmasse N, Schmandt C (2000) Location-aware information delivery with commotion. In: Proceedings of the 2nd international symposium on Handheld and Ubiquitous Computing, HUC ’00, Springer, pp 157–171
Palma AT, Bogorny V, Kuijpers B, Alvares LO (2008) A clustering-based approach for discovering interesting places in trajectories. In: Proceedings of the 2008 ACM Symposium on Applied Computing, SAC ’08, ACM, pp 863–868
Ram A, Jalal S, Jalal AS, Kumar M (2010) A density based algorithm for discovering density varied clusters in large spatial databases. Int J Comput Appl 3(6):1–4 (Published by Foundation of Computer Science)
Zheng Y, Zhang L, Xie X, Ma W-Y (2009) Mining interesting locations and travel sequences from gps trajectories. In: ‘Proceedings of the 18th international conference on World Wide Web’, WWW ’09, ACM, pp 791–800
Zhou C, Frankowski D, Ludford P, Shekhar S, Terveen L (2007) Discovering personally meaningful places: an interactive clustering approach. ACM Trans Inf Syst 25(3):56–68
Zhang YF, Zhang QF, Yu RH (2010) Markov property of markov chains and its test. In: Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), IEEE, pp 1864–1867
Ekstrand MD, Riedl JT, Konstan JA (2011) Collaborative filtering recommender systems. Found Trends R in Hum-Comput Interact 4(2):81–173
Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, WWW ’01, ACM, New York, NY, USA, pp 285–295
Karypis G (2001) Evaluation of item-based top-n recommendation algorithms. In Proceedings of the tenth international Conference on Information and Knowledge Management, CIKM ’01, ACM, New York, NY, USA, pp 247–254
Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the fourteenth conference on Uncertainty in Artificial Intelligence, UAI ’98, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 43–52
Zheng Y, Li Q, Chen Y, Xie X, Ma W-Y (2008) Understanding mobility based on gps data. In: Proceedings of the 10th international conference on Ubiquitous Computing, UbiComp ’08, ACM, pp 312–321
Zheng Y, Xie X, Ma W-Y (2010) Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng Bull 33(2):32–39
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Alvarez-Lozano, J., García-Macías, J.A., Chávez, E. (2018). User Location Forecasting Based on Collective Preferences. In: Sanchez, M., Aguilar, L., Castañón-Puga, M., Rodríguez-Díaz, A. (eds) Computer Science and Engineering—Theory and Applications. Studies in Systems, Decision and Control, vol 143. Springer, Cham. https://doi.org/10.1007/978-3-319-74060-7_13
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