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Predicting user behavior is the cornerstone of intelligent services and applications for providing and optimizing services over mobile networks. In modern edge computing scenarios, contents and services will be ordered close to end-users and will be highly sensitive to user mobility. Deep Learning models have had significant success in performing prediction tasks. However, providing reliable predictions for real-world networks in scale requires the Neural Architecture Search to be optimized on a user basis. In this work, we present an LSTM-based mobility predictor to improve the trajectory prediction accuracy. To speed up the model convergence rate, we employ a reinforcement learning technique to automate the selection procedure of the best neural network architecture. To further accelerate the reinforcement learning environmental search procedure, we transfer the architecture knowledge learned from a …