  Modeling the sequential correlation of users' historical interactions is
essential in sequential recommendation. However, the majority of the approaches
mainly focus on modeling the \emph{intra-sequence} item correlation within each
individual sequence but neglect the \emph{inter-sequence} item correlation
across different user interaction sequences. Though several studies have been
aware of this issue, their method is either simple or implicit. To make better
use of such information, we propose an inter-sequence enhanced framework for
the Sequential Recommendation (ISSR). In ISSR, both inter-sequence and
intra-sequence item correlation are considered. Firstly, we equip graph neural
networks in the inter-sequence correlation encoder to capture the high-order
item correlation from the user-item bipartite graph and the item-item graph.
Then, based on the inter-sequence correlation encoder, we build GRU network and
attention network in the intra-sequence correlation encoder to model the item
sequential correlation within each individual sequence and temporal dynamics
for predicting users' preferences over candidate items. Additionally, we
conduct extensive experiments on three real-world datasets. The experimental
results demonstrate the superiority of ISSR over many state-of-the-art methods
and the effectiveness of the inter-sequence correlation encoder.
