RNN details
We use a simple RNN architecture with a single LSTM layer and ten-dimensional cell states. The hidden state at the last time-step is combined with binary non-history features to make the final prediction in a logistic layer. Thus, the final prediction of the RNN is linear in the learned and non-history features. The non-history features describe time, weekday, and behavioral gender and are also provided to the baseline methods. Instead of absolute timestamps, the time differences ∆(x t−1 , x t ) to the previous inputs x t−1 are fed to the RNN at each time- step t. Furthermore, the difference between the last event x T and the prediction time (the [...]) is provided to the final prediction layer