  Modeling users' preference from his historical sequences is one of the core
problem of sequential recommendation. Existing methods in such fields are
widely distributed from conventional methods to deep learning methods. However,
most of them only model users' interests within their own sequences and ignore
the fine-grained utilization of dynamic collaborative signals among different
user sequences, making them insufficient to explore users' preferences. We take
inspiration from dynamic graph neural networks to cope with this challenge,
unifying the user sequence modeling and dynamic interaction information among
users into one framework. We propose a new method named \emph{Dynamic Graph
Neural Network for Sequential Recommendation} (DGSR), which connects the
sequence of different users through a dynamic graph structure, exploring the
interactive behavior of users and items with time and order information.
Furthermore, we design a Dynamic Graph Attention Neural Network to achieve the
information propagation and aggregation among different users and their
sequences in the dynamic graph. Consequently, the next-item prediction task in
sequential recommendation is converted into a link prediction task for the user
node to the item node in a dynamic graph. Extensive experiments on four public
benchmarks show that DGSR outperforms several state-of-the-art methods. Further
studies demonstrate the rationality and effectiveness of modeling user
sequences through a dynamic graph.
