A General Multi-Context Embedding Model for Mining Human Trajectory Data

@article{Zhou2016AGM,
  title={A General Multi-Context Embedding Model for Mining Human Trajectory Data},
  author={Ningnan Zhou and Wayne Xin Zhao and Xiao Zhang and Ji-Rong Wen and Shan Wang},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2016},
  volume={28},
  pages={1945-1958}
}
The proliferation of location-based social networks, such as Foursquare and Facebook Places, offers a variety of ways to record human mobility, including user generated geo-tagged contents, check-in services, and mobile apps. Although trajectory data is of great value to many applications, it is challenging to analyze and mine trajectory data due to the complex characteristics reflected in human mobility, which is affected by multiple contextual information. In this paper, we propose a Multi… CONTINUE READING

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