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Volume 31 Issue 4
July  2016
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Meng Chen, Xiaohui Yu, Yang Liu. Mining Object Similarity for Predicting Next Locations[J]. Journal of Computer Science and Technology, 2016, 31(4): 649-660. DOI: 10.1007/s11390-016-1654-2
Citation: Meng Chen, Xiaohui Yu, Yang Liu. Mining Object Similarity for Predicting Next Locations[J]. Journal of Computer Science and Technology, 2016, 31(4): 649-660. DOI: 10.1007/s11390-016-1654-2
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Mining Object Similarity for Predicting Next Locations

  • 1 School of Computer Science and Technology, Shandong University, Jinan 250101, China;
    2 School of Information Technology, York University, Toronto, M3 J1 P3, Canada

Funds: This work was supported in part by the National Basic Research 973 Program of China under Grant No. 2015CB352502, the National Natural Science Foundation of China under Grant Nos. 61272092 and 61572289, the Natural Science Foundation of Shandong Province of China under Grant Nos. ZR2012FZ004 and ZR2015FM002, the Science and Technology Development Program of Shandong Province of China under Grant No. 2014GGE27178, and the NSERC (Natural Sciences and Engineering Research Council of Canada) Discovery Grants.
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  • Author Bio:

    Meng Chen received his B.S. degree in software engineering in 2011 from Shandong University, Jinan. He is currently a Ph.D. candidate in the School of Computer Science and Technology, Shandong University, Jinan. His research interest is in the area of data mining.

  • Corresponding author:

    Yang Liu E-mail: yliu@sdu.edu.cn

  • Received Date: February 25, 2016
  • Revised Date: May 26, 2016
  • Published Date: July 04, 2016
    Abstract
  • Next location prediction is of great importance for many location-based applications. With the virtue of solid theoretical foundations, Markov-based approaches have gained success along this direction. In this paper, we seek to enhance the prediction performance by understanding the similarity between objects. In particular, we propose a novel method, called weighted Markov model (weighted-MM), which exploits both the sequence of just-passed locations and the object similarity in mining the mobility patterns. To this end, we first train a Markov model for each object with its own trajectory records, and then quantify the similarities between different objects from two aspects:spatial locality similarity and trajectory similarity. Finally, we incorporate the object similarity into the Markov model by considering the similarity as the weight of the probability of reaching each possible next location, and return the top-rankings as results. We have conducted extensive experiments on a real dataset, and the results demonstrate significant improvements in prediction accuracy over existing solutions.
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