A Neural Time Series Forecasting Model for User Interests Prediction On Twitter

Time plays a crucial role in influencing and understanding users' changeable preferences among various factors which directly or indirectly result in users' interesting behavior. In this paper, we propose a neural time series forecasting model (NTSF) to fit and predict user preference trend according to time. In this model, we integrate emerging/hot topic detection to deal with the short-term aspects, and use Fast Fourier Transformation (FFT) to differentiate cyclic behavior of users. Considering the nonstationary and nonlinear characteristics appearing through user interest patterns, Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) is employed to balance the influence of short and long term aspects, as well as adjust model parameters according to historical results. Our experimental results with extensive Twitter datasets verify the effectiveness of our approach.