It can be seen that the last user in the figure (the one at the bottom line) has the greatest values for ✏ and , which means that the decay factor has a great impact on the prediction for this user than the others but the influence from the past decays comparably fastthe user is affected a lot by recent events.
after that, the outputs of the recurrent layer together with the auxiliary time differences are fed to the attention block.