  The chronological order of user-item interactions can reveal time-evolving
and sequential user behaviors in many recommender systems. The items that users
will interact with may depend on the items accessed in the past. However, the
substantial increase of users and items makes sequential recommender systems
still face non-trivial challenges: (1) the hardness of modeling the short-term
user interests; (2) the difficulty of capturing the long-term user interests;
(3) the effective modeling of item co-occurrence patterns. To tackle these
challenges, we propose a memory augmented graph neural network (MA-GNN) to
capture both the long- and short-term user interests. Specifically, we apply a
graph neural network to model the item contextual information within a
short-term period and utilize a shared memory network to capture the long-range
dependencies between items. In addition to the modeling of user interests, we
employ a bilinear function to capture the co-occurrence patterns of related
items. We extensively evaluate our model on five real-world datasets, comparing
with several state-of-the-art methods and using a variety of performance
metrics. The experimental results demonstrate the effectiveness of our model
for the task of Top-K sequential recommendation.
