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Mathematical Problems in Engineering
Volume 2013, Article ID 380871, 9 pages
Research Article

Interesting Activities Discovery for Moving Objects Based on Collaborative Filtering

School of Computer Science and Technology, China University of Mining and Technology, No. 1, College Road, Xuzhou, Jiangsu 221116, China

Received 19 April 2013; Revised 16 June 2013; Accepted 6 July 2013

Academic Editor: Saeed Balochian

Copyright © 2013 Guan Yuan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the development of location-based service, more and more moving objects can be traced, and a great deal of trajectory data can be collected. Finding and studying the interesting activities of moving objects from these data can help to learn their behavior very well. Therefore, a method of interesting activities discovery based on collaborative filtering is proposed in this paper. First, the interesting degree of the objects' activities is calculated comprehensively. Then, combined with the newly proposed hybrid collaborative filtering, similar objects can be computed and all kinds of interesting activities can be discovered. Finally, potential activities are recommended according to their similar objects. The experimental results show that the method is effective and efficient in finding objects' interesting activities.