# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 4 Experiments
### Ablation Studies
#### 4.6.2 Effect of Modules  
To assess the effectiveness and robustness of the proposed modules, we decoupled UPGCN. Specifically, we separated the designed modules and methods from UPGCN to investigate their impact on recommendation performance; consequently, we constructed the following variants:  
* UPGCN-B: This variant removes the UPEM and multimodal enhanced fusion method that we designed; \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Variants** & **R@10** & **R@20** & **N@10** & **N@20** \\ \hline \multirow{4}{*}{Baby} & \(\text{UPGCN}_{\text{w/o-vk}}\) & 0.0479 & 0.0754 & 0.0257 & 0.0328 \\  & \(\text{UPGCN}_{\text{w/o-t}}\) & 0.0527 & 0.0827 & 0.0282 & 0.0359 \\  & \(\text{UPGCN}_{\text{w/o-v}}\) & 0.0616 & 0.0984 & 0.0328 & 0.0422 \\  & \(\text{UPGCN}\) & 0.0664 & 0.1043 & 0.0351 & 0.0449 \\ \hline \multirow{4}{*}{Sports} & \(\text{UPGCN}_{\text{w/o-vk}}\) & 0.0569 & 0.0864 & 0.0311 & 0.0387 \\  & \(\text{UPGCN}_{\text{w/o-t}}\) & 0.0610 & 0.0931 & 0.0332 & 0.0415 \\  & \(\text{UPGCN}_{\text{w/o-v}}\) & 0.0716 & 0.1079 & 0.0391 & 0.0484 \\  & \(\text{UPGCN}\) & 0.0740 & 0.1114 & 0.0402 & 0.0498 \\ \hline \multirow{4}{*}{Clothing} & \(\text{UPGCN}_{\text{w/o-vk}}\) & 0.0340 & 0.0526 & 0.0188 & 0.0236 \\  & \(\text{UPGCN}_{\text{w/o-t}}\) & 0.0430 & 0.0633 & 0.0229 & 0.0281 \\ \cline{1-1}  & \(\text{UPGCN}_{\text{w/o-v}}\) & 0.0616 & 0.0927 & 0.0336 & 0.0415 \\ \cline{1-1}  & \(\text{UPGCN}\) & 0.0644 & 0.0959 & 0.0348 & 0.0428 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study of UPGCN on multimodal features. * UPGCN-E: This variant only uses UPEM to enhance the user representation, and removes the two-step enhanced fusion method;
* UPGCN-T: This variant only uses the multimodal two-step enhanced fusion method to enhance and fuse the item representations in different modalities, removing UPEM. The comparison results for R@20 and N@20 are presented in Figure 2. By comparing the performance of UPGCN-E and UPGCN-B, it is evident that incorporating the UPEM has resulted in significant enhancements in both Recall@20 and NDCG@20 across all datasets. This indicates that UPEM has played a positive role in capturing user preference information and improving recommendation performance. Comparing UPGCN-T with UPGCN-B shows that the multimodal enhanced fusion method has also significantly improved the model's performance in Recall@20 and NDCG@20. In particular, the multimodal enhanced fusion method has a particularly noticeable impact on improving recommendation performance in the Clothing dataset, further indicating that the method can effectively improve recommendation results on large-scale datasets. UPGCN outperforms all other variants across all datasets regarding Recall@20 and NDCG@20, demonstrating its superior performance. This indicates that the combination of UPEM and multimodal enhanced fusion methods can synergistically enhance model performance. Moreover, the integration of these two methods is an important component of our model, which helps to better capture user preferences and effectively distinguish the similarity between multimodal features, thereby highlighting the single-modal features while fully utilizing the complementarity of different modalities to obtain a more accurate item representation and ultimately improve recommendation performance. Furthermore, in the Baby and Clothing datasets, UPGCN-T performs slightly better than UPGCN-E, indicating that the multimodal enhanced fusion method has a relatively greater impact on these two datasets. However, in the Sports dataset, UPGCN-T performs slightly worse than UPGCN-E in terms of Recall@20 and NDCG@20. We believe that one potential reason for this is that the Sports dataset extracts less information from product text descriptions and images compared to the other two datasets, and therefore, the two-step enhanced fusion method that utilizes multimodal features does not perform well in this dataset.