Mining GPS data for trajectory recommendation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8444 LNAI, Issue PART 2, 2014, Pages 50-61

  Yin, Peifeng ^a   Ye, Mao ^b   Lee, Wang Chien ^a   Li, Zhenhui ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b Pinterest ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING;

ACTIVITY-BASED; BUILDING USERS; GENERATIVE MODEL; HYBRID RECOMMENDATION; HYBRID SOLUTION; NEW FORMATS; RECOMMENDATION METHODS; USER PROFILE;

TRAJECTORIES;

EID: 84901290423     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-06605-9_5     Document Type: Conference Paper

References (22)

1. Balabanovic, M., Shoham, Y.: Content-based collaborative recommendation. CACM 40(3), 66-72 (1997)
2. Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1-27:27 (2011), http://www.csie.ntu.edu.tw/~cjlin/libsvm
3. Chen, Z., Shen, H.T., Zhou, X.: Discovering popular routes from trajectories. In: ICDE (2011)
4. Chen, Z., Shen, H.T., Zhou, X., Zheng, Y., Xie, X.: Searching trajectories by locations: An efficiency study. In: SIGMOD, pp. 255-266 (2010)
5. Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society 39(1), 1-38 (1977)
6. Dodge, S.,Weibel, R., Forootan, E.: Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects. Computers, Environment and Urban Systems 33(6), 419-434 (2009)
7. Dodge, S.,Weibel, R., Laube, P.: Exploring movement-similarity analysis of moving objects. SIGSPATIAL Special 1, 11-16 (2009)
8. Ferman, A.M., Errico, J.H., van Beek, P., Sezan, M.I.: Content-based filtering and personalization using structured metadata. In: JCDL, p. 393 (2002)
9. Li, X., Hu, W., Hu, W.: A coarse-to-fine strategy for vehicle motion trajectory clustering. In: Pattern Recognition (2006)
10. Li, Z., Ding, B., Han, J., Kays, R., Nye, P.: Mining periodic behaviors for moving objects. In: KDD, pp. 1099-1108 (2010)
11. Li, Z., Han, J., Ji, M., Tang, L.-A., Yu, Y., Ding, B., Lee, J.-G., Kays, R.: Movemine: Mining moving object data for discovery of animal movement patterns. TIST (2010)
12. Logan, B.: Mel frequency cepstral coefficients for music modeling. In: ISMIR (2000)
13. Melville, P., Mooney, R.J., Nagarajan, R.: Content-Boosted Collaborative Filtering for Improved Recommendations. In: AAAI/IAAI, pp. 187-192 (2002)
14. Naftel, A., Khalid, S.: Classifying spatiotemporal object trajectories using unsupervised learning in the coefficient feature space. Multimedia Systems 12(3), 227-238 (2006)
15. Naftel, A., Khalid, S.: Motion trajectory learning in the dft-coefficient feature space. In: IEEE Conf. Comput. Vision Syst. (2006)
16. Pfeiffer, S., Vincent, T.: Formalisation of mpeg-1 compressed-domain audio features. Technical report, CSIRO mathematical and information, sciences, Australia (2001)
17. Reddy, S., Burke, J., Estrin, D., Hansen, M., Srivastava, M.: Determining transportation mode on mobile phones. In: ISWC (2008)
18. Reddy, S., Mun, M., Burke, J., Estrin, D., Hansen, M., Srivastava, M.: Using mobile phones to determine transportation modes. ACM Trans. Sen. Netw. 6, 13:1-13:27 (2010)
19. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285-295 (2001)
20. Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Collaborative location and activity recommendations with gps history data. In: WWW, pp. 1029-1038 (2010)
21. Zheng, Y., Chen, Y., Li, Q., Xie, X., Ma, W.-Y.: Understanding transportation modes based on gps data for web applications. ACM Trans. Web 4, 1:1-1:36 (2010)
22. Zheng, Y., Liu, L., Wang, L., Xie, X.: Learning transportation mode from raw gps data for geographic applications on the web. In: WWW, pp. 247-256 (2008)