DGSR: Dual-Graph Sequential Recommendation with Gated and Heterogeneous GNNs

Sequential recommendation represents a well-explored yet challenging domain within research. Despite significant advancements in GNN-based methods for modeling intricate patterns in user-item interaction sequences, these methods face difficulties in capturing nuanced semantics in sequences with sparse dependencies and noise, and often struggle with short sequences. Additionally, distinguishing higher-order semantic distinctions among diverse user interests is still challenging, and existing GNN-based methods can be computationally intensive. To address these challenges, we propose a Dual-Graph approach for Sequential Recommendation, DGSR. We construct individual interaction graphs for each sequence, and a heterogeneous global interaction graph that incorporates user identity as an attribute of user edges. Last-item augmented GGNN is employed within individual interaction graphs to mitigate the impact of sparse dependencies and noise, thereby extracting the most recent interests for each user more effectively. Moreover, within the global graph, we propose a parameter-efficient heterogeneous GNN to extract high-order interest distinctions among diverse users while maintaining low computational complexity. Finally, we utilize vanilla transform mechanism to integrate intra- and inter-user interests from both types of graphs. Experiments on four publicly available datasets demonstrate that our method achieves state-of-the-art performance, surpassing all baseline methods.