  Sequential recommendation has been a widely popular topic of recommender
systems. Existing works have contributed to enhancing the prediction ability of
sequential recommendation systems based on various methods, such as recurrent
networks and self-attention mechanisms. However, they fail to discover and
distinguish various relationships between items, which could be underlying
factors which motivate user behaviors. In this paper, we propose an
Edge-Enhanced Global Disentangled Graph Neural Network (EGD-GNN) model to
capture the relation information between items for global item representation
and local user intention learning. At the global level, we build a global-link
graph over all sequences to model item relationships. Then a channel-aware
disentangled learning layer is designed to decompose edge information into
different channels, which can be aggregated to represent the target item from
its neighbors. At the local level, we apply a variational auto-encoder
framework to learn user intention over the current sequence. We evaluate our
proposed method on three real-world datasets. Experimental results show that
our model can get a crucial improvement over state-of-the-art baselines and is
able to distinguish item features.
