Abstract
Personalized travel route recommendation refers to the planning of an optimal travel route between two geographical locations based on the road networks and users’ travel preferences. In this paper, we extract users’ travel behaviours from their historical GPS trajectories and propose a personalized maximum probability route recommendation method called MaP2R. MaP2R utilizes the concepts of appearance behaviour and transition behaviour to describe users’ travel behaviours and applies matrix factorization and Laplace smoothing method to estimate users’ travel behaviour probabilities. When making recommendation, a route with the maximum probability of a user’s travel behaviours is generated based on Markov property and searched through a generated behaviour graph. The experimental results on a real GPS trajectory dataset show that the proposed MaP2R achieves better results for travel route recommendations compared with the existing methods.
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
Similar content being viewed by others
References
Chen, Z., Shen, H.T., Zhou, X.: Discovering popular routes from trajectories. In: 27th IEEE International Conference on Data Engineering, pp. 900–911. IEEE Press (2011)
Dai, J., Yang, B., Guo, C., Ding, Z.: Personalized route recommendation using big trajectory data. In: IEEE 31st International Conference on Data Engineering, pp. 543–554. IEEE Press (2015)
Delling, D., Wagner, D.: Pareto paths with SHARC. In: Vahrenhold, J. (ed.) SEA 2009. LNCS, vol. 5526, pp. 125–136. Springer, Heidelberg (2009). doi:10.1007/978-3-642-02011-7_13
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42, 30–37 (2009)
Letchner, J., Krumm, J., Horvitz, E.: Trip router with individualized preferences (TRIP): incorporating personalization into route planning. In: Proceedings of the 18th AAAI Conference on Innovative Applications of Artificial Intelligence, pp. 1795–1800. AAAI Press, Palo Alto (2006)
Newson, P., Krumm, J.: Hidden Markov map matching through noise and sparseness. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. pp. 336–343. ACM Press, New York (2009)
Yang, W., Wang, X., Rahimi, S.M., Luo, J.: Recommending profitable taxi travel routes based on big taxi trajectories data. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS (LNAI), vol. 9078, pp. 370–382. Springer, Cham (2015). doi:10.1007/978-3-319-18032-8_29
Zheng, V.W., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: a user-centered approach. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence, pp. 236–241. AAAI Press, Palo Alto (2010)
Zheng, Y., Xie, X.: Learning travel recommendations from user-generated GPS traces. ACM Trans. Intell. Syst. Technol. (TIST) 2, 1–29 (2011)
Acknowledgements
The research is supported by the Natural Sciences and Engineering Research Council of Canada Discovery Grant to Xin Wang and National Natural Science Foundation of China (No. 41271387).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Cui, G., Wang, X. (2017). MaP2R: A Personalized Maximum Probability Route Recommendation Method Using GPS Trajectories. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10235. Springer, Cham. https://doi.org/10.1007/978-3-319-57529-2_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-57529-2_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57528-5
Online ISBN: 978-3-319-57529-2
eBook Packages: Computer ScienceComputer Science (R0)