  Most sequential recommendation (SR) systems employing graph neural networks
(GNNs) only model a user's interaction sequence as a flat graph without
hierarchy, overlooking diverse factors in the user's preference. Moreover, the
timespan between interacted items is not sufficiently utilized by previous
models, restricting SR performance gains. To address these problems, we propose
a novel SR system employing a hierarchical graph neural network (HGNN) to model
factorial user preferences. Specifically, a timespan-aware sequence graph (TSG)
for the target user is first constructed with the timespan among interacted
items. Next, all original nodes in TSG are softly clustered into factor nodes,
each of which represents a certain factor of the user's preference. At last,
all factor nodes' representations are used together to predict SR results. Our
extensive experiments upon two datasets justify that our HGNN-based factorial
user modeling obtains better SR performance than the state-of-the-art SR
models.
