Graph-based Dynamic Preference Modeling for Personalized Recommendation

Sequential Recommendation (SR) can predict possible future behaviors by considering the user's behavioral sequence. However, users' preferences constantly change in practice and are difficult to track. The existing methods only consider neighbouring items and neglect the impact of non-adjacent items on user choices. Therefore, how to build an accurate recommendation model is a complex challenge. We propose a novel Graph Neural Network (GNN) based model, Graph-based Dynamic Preference Modeling for Personalized Recommendation (DPPR). In DPPR, the graph attention network (GAT) learns the features of long-term preference. The short-term graph computes items' dependencies on link propagation between items and attributes. It adjusts node features under the user's views. The module emphasizes skip features among entity nodes and incorporates time intervals of items to calculate the impact of non-adjacent items. Finally, we combine their representations to generate user preferences and aid decisions. The experimental results indicate that our model outperforms state-of-the-art methods on three public datasets.