Dynamic Graph Neural Networks for Sequential Recommendation

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