## 5 Experimental Setup
### Ablation Study  
To evaluate the effectiveness of each module in the GEVEHGAN model, we conduct an ablation study using two representative datasets: NYC and TKY. A series of experiments are designed to evaluate the impact of the node feature learning module, VAE module, the edge-type attention mechanism, and the node residual connection mechanism. The specific experimental setups are as follows:  
* GEVEHGAN-Init: The node feature initialization module in the input layer is removed, and the heterogeneous graph node features are randomly initialized with small values. Other parts of the model remain unchanged. * GEVEHGAN-Vae: The Variational Autoencoder module is removed, and the initialized features are directly used. Other parts of the model remain unchanged. * GEVEHGAN-Att: The edge-type attention mechanism in the node feature learning process is removed, and the importance of neighbor nodes remains the same. Other parts of the model remain unchanged. * GEVEHGAN-Res: The node pre-activation residual connection mechanism is removed, and the traditional GNN node aggregation update method is used. Other parts of the model remain unchanged. The results of the ablation study are shown in Figure 5. The experiments analyze the impact of each component on model performance, as detailed below:* Node Feature Embedding Module: After removing the node feature embedding module, the node's initial features loses the semantic associations and temporal patterns obtained through pretraining, resulting in a reduction in the information available to the model when understanding user behavior and interests. The three metrics of the GEVEHGAN-Init model decrease by 4.11%, 4.51%, and 3.81%, respectively. This indicates that the features learned through Skip-Gram and Lite-GRU as the initial node features for the heterogeneous graph provide richer information for the nodes, significantly enhancing the model's performance. * Variational Autoencoder Module: After removing the VAE module, the model fails to perform noise reduction on high-dimensional sparse features, leading to more irrelevant data being included in the information, thus reducing the clarity and usability of the features. The three metrics of the GEVEHGAN-Vae model decrease by 2.17%, 2.47%, and 2.42%, respectively. This shows that the VAE module condenses the key information from the original node features, removes redundancy and noise, and provides the model with more refined feature representations. * Edge-Type Attention Mechanism: After removing the edge-type attention mechanism, the model is unable to dynamically adjust the weights of the edges, resulting in an imbalance in the propagation of information across multiple relations, with key information not receiving enough attention. The three metrics of the GEVEHGAN-Att model decrease by 5.45%, 5.76%, and 4.42%, respectively. This demonstrates that the edge-type attention mechanism helps focus on key edge information, integrates multiple relationships to enhance feature representation, and improves information utilization efficiency through adaptive information selection and optimized propagation paths. * Node Residual Connection Mechanism: After removing the node residual connection mechanism, the information gradually diminishes during propagation through the deeper layers of the network, causing the model to lose the ability to maintain differentiation between nodes, leading to gradient vanishing and learning difficulties during training. The three metrics of the GEVEHGAN-Res model decrease by 3.06%, 3.87%, and 3.51%, respectively. This suggests that the node residual connection mitigates gradient issues, improves backpropagation, facilitates parameter updates, and prevents information loss. Additionally, it further improves feature fusion, leading to better representations and enhanced model performance. Ablation experiments show that the four core components of GEVEHGAN make significant contributions to performance.