Hi,
I am building a recommencer system using DGL. Currently, there is little temporality involved in generating recommendations for users. However, I would like to model the task in a sequential fashion, i.e. use all K previous items at time t to predict the next items that a user might interact with, in the fashion of a RNN.
To do so, I would like that when I create the computational blocks for my training edges, all edges that are more recent than the training edge are excluded (and would be considered as the ground truth).
Currently, even if I incorporate sequential message passing techniques like LSTM aggregation, “future edges” will be included in the message passing, since they are part of the sampling graph.
As I browsed the multiple DGL functionalities, I understood that this might not be a trivial task. Would anyone have advices on how to do so?
Thanks in advance!