Precision in predicting people's next location plays a key role in the success of advanced Location-Based Services (LBSs). Models in the literature have achieved satisfactory results, but they lack exploiting of timestamps-sensitive property while combining it with locations sequences. In this paper, we propose a location prediction model in which time encoding scheme is proposed to capture movement behavior characteristics. Embedding learning technique and neural pooling functions are used to extract the semantic information of input data. A set of neural pooling functions are explored in order to extract rich features. Recurrent Neural Network (RNN) is utilized in the proposed model in order to keep track of user movement history which allows to discover more meaningful dependencies. As consequence, the model performance is enhanced. Evaluations on a large real life dataset demonstrate that the proposed model outperforms state-of-the-art models in terms of Recall, Precision and F1-score performance metrics.
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