Abstract: In order to solve the problem of ignoring the character of user behavior sequence and limiting the improvement of prediction accuracy, two location prediction models based on the character of user behavior sequence were proposed. Firstly, behavior+context+profile+RNN (BCP-RNN) model is constructed by manually extracting sequence features of user behaviors and integrating the features into the location prediction model. Then three-layer symmetrical neural network (TS-RNN) model is constructed by automatically learning behavior sequence features based on the recurrent structure of RNN model and integrating the features into location prediction model. Experiments show that, compared with the existing location prediction models, BCP-RNN and TS-RNN improves the prediction performance, verifying the importance of behavior sequence features in mining user movement patterns. Besides, compared with the BCP-RNN model of manually extracting behavior sequence features, TS-RNN not only saves the cost of artificial feature extraction, but also makes up for the deviation caused by one-sided human analysis, and has higher prediction accuracy.

Key words: location prediction, location semantic, sequence features of behaviors