# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 4 Experiments
### Hyperparameter Sensitivity Study
#### 4.7.2 Effects of the Weight for Multimodal BPR Loss \(\lambda_{\text{reg}}\)  
In the experiment of hyperparameter \(\lambda_{\text{reg}}\), we systematically explored the impact of different \(\lambda_{\text{reg}}\) values on Recall@20 and NDCG@20, aiming to study the role of balancing multimodal BPR loss and standard BPR loss on recommendation performance. As shown in Figure 4, in the Baby and Clothing datasets, as \(\lambda_{\text{reg}}\) is increased from 1 \(\times\) 10\({}^{-5}\) to 0.1 in steps, the changes in Recall@20 and NDCG@20 exhibit a complex nonlinear trend, reflecting the model's sensitivity to the weight of the multimodal loss.  
In the Baby dataset, the Recall@20 and NDCG@20 metrics show a noticeable fluctuation within a small range of \(\lambda_{\text{reg}}\). Initially, Recall@20 is close to 0.1035 when \(\lambda_{\text{reg}}\) is 1 \(\times\) 10\({}^{-5}\), and both Recall@20 and NDCG@20 slightly decrease as \(\lambda_{\text{reg}}\) increases to 1 \(\times\) 10\({}^{-3}\), indicating that the model's dependence on multimodal features has not been fully stimulated at this point. When \(\lambda_{\text{reg}}\) is increased to 1 \(\times\) 10\({}^{-2}\), both Recall@20 and NDCG@20 reach relatively high values simultaneously, with Recall@20 at 0.1043 and NDCG@20 at 0.0451. This result shows that the introduction of multimodal BPR loss with moderate weight strength effectively enhances the model's recommendation performance, especially when user and item features from different modalities are fully fused. However, when \(\lambda_{\text{reg}}\) is increased to 0.1, performance slightly declines again, indicating that the model may suppress multimodal features if it pays too much attention to feature extractor optimization,  
Figure 3: The effects of the fusion weight.  
preventing the model from fully learning the multimodal information that is effective in predicting user preferences.  
In the Clothing dataset, Recall@20 and NDCG@20 also show similar trends of change. When \(\lambda_{reg}=1\ \times 10^{-5}\), Recall@20 and NDCG@20 are 0.0946 and 0.0423, respectively. As \(\lambda_{reg}\) increases to \(1\ \times 10^{-4}\), Recall@20 slightly increases to 0.0959, which is the peak of Recall@20 on the Clothing dataset. For NDCG@20, the optimal value also appears at \(\lambda_{reg}=1\ \times\ 10^{-4}\), with NDCG@20 reaching 0.0428. This indicates that when \(\lambda_{reg}\) is small, the model can better balance the standard BPR loss and multimodal BPR loss, allowing the model to effectively utilize multimodal features to enhance recommendation accuracy. However, when \(\lambda_{reg}\) is further increased to \(1\times 10^{-2}\) and 0.1, Recall@20 and NDCG@20 both decrease, indicating that excessive optimization of the feature extractor has the opposite effect on the utilization of multimodal features.  
Overall, the experimental results show that moderate \(\lambda_{reg}\) can achieve a good balance between BPR loss and multimodal BPR loss, thereby improving the recommendation performance of the model. In particular, when \(\lambda_{reg}\) is moderate, the model can fully utilize multimodal information to enhance the representation of users and items, thereby improving the recommendation accuracy. However, when \(\lambda_{reg}\) is too large, the excessively high weight of the multimodal BPR loss may lead to the excessive suppression of multimodal features, thereby reducing the learning effect of the model. Therefore, the experimental results suggest that \(\lambda_{reg}\) should be set to a medium range of values to ensure that the model can effectively integrate information across different modalities while avoiding the negative effects of excessively optimizing the feature extractor.