  Trajectory owner prediction is the basis for many applications such as
personalized recommendation, urban planning. Although much effort has been put
on this topic, the results archived are still not good enough. Existing methods
mainly employ RNNs to model trajectories semantically due to the inherent
sequential attribute of trajectories. However, these approaches are weak at
Point of Interest (POI) representation learning and trajectory feature
detection. Thus, the performance of existing solutions is far from the
requirements of practical applications. In this paper, we propose a novel
CNN-based Trajectory Owner Prediction (CNNTOP) method. Firstly, we connect all
POI according to trajectories from all users. The result is a connected graph
that can be used to generate more informative POI sequences than other
approaches. Secondly, we employ the Node2Vec algorithm to encode each POI into
a low-dimensional real value vector. Then, we transform each trajectory into a
fixed-dimensional matrix, which is similar to an image. Finally, a CNN is
designed to detect features and predict the owner of a given trajectory. The
CNN can extract informative features from the matrix representations of
trajectories by convolutional operations, Batch normalization, and $K$-max
pooling operations. Extensive experiments on real datasets demonstrate that
CNNTOP substantially outperforms existing solutions in terms of
macro-Precision, macro-Recall, macro-F1, and accuracy.
