  The chronological order of user-item interactions is a key feature in many
recommender systems, where the items that users will interact may largely
depend on those items that users just accessed recently. However, with the
tremendous increase of users and items, sequential recommender systems still
face several challenging problems: (1) the hardness of modeling the long-term
user interests from sparse implicit feedback; (2) the difficulty of capturing
the short-term user interests given several items the user just accessed. To
cope with these challenges, we propose a hierarchical gating network (HGN),
integrated with the Bayesian Personalized Ranking (BPR) to capture both the
long-term and short-term user interests. Our HGN consists of a feature gating
module, an instance gating module, and an item-item product module. In
particular, our feature gating and instance gating modules select what item
features can be passed to the downstream layers from the feature and instance
levels, respectively. Our item-item product module explicitly captures the item
relations between the items that users accessed in the past and those items
users will access in the future. We extensively evaluate our model with several
state-of-the-art methods and different validation metrics on five real-world
datasets. The experimental results demonstrate the effectiveness of our model
on Top-N sequential recommendation.
