# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 4 Experiments
### Performance Comparison with Baselines  
Table 2 illustrates a comparison of the recommendation performance between the presented methods and our proposed model, UPGCN. Specifically, we used FREEDOM as the baseline method for comparison. UPGCN made an improvement in effectiveness compared to FREEDOM, which is expressed as a percentage of the improvement. The table reveals the subsequent observations: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Model** & **R@10** & **R@20** & **N@10** & **N@20** \\ \hline \multirow{8}{*}{Baby} & BPR & 0.0357 & 0.0575 & 0.0192 & 0.0249 \\  & LightGCN & 0.0479 & 0.0754 & 0.0257 & 0.0328 \\ \cline{2-6}  & VBPR & 0.0423 & 0.0663 & 0.0223 & 0.0284 \\  & MMGCN & 0.0421 & 0.0660 & 0.0220 & 0.0282 \\  & GRCN & 0.0532 & 0.0824 & 0.0282 & 0.0358 \\  & DualGNN & 0.0513 & 0.0803 & 0.0278 & 0.0352 \\  & SLMRec & 0.0521 & 0.0772 & 0.0289 & 0.0354 \\  & LATTICE & 0.0547 & 0.0850 & 0.0292 & 0.0370 \\  & FREEDOM & 0.0627 & 0.0992 & 0.0330 & 0.0424 \\  & UPGCN & 0.0664 & 0.1043 & 0.0351 & 0.0449 \\  & Improv. (\%) & 4.31 & 4.53 & 5.76 & 5.42 \\ \hline \multirow{8}{*}{Sports} & BPR & 0.0432 & 0.0653 & 0.0241 & 0.0298 \\  & LightGCN & 0.0569 & 0.0864 & 0.0311 & 0.0387 \\ \cline{1-1} \cline{2-6}  & VBPR & 0.0558 & 0.0856 & 0.0307 & 0.0384 \\ \cline{1-1}  & MMGCN & 0.0401 & 0.0636 & 0.0209 & 0.0270 \\ \cline{1-1}  & GRCN & 0.0599 & 0.0919 & 0.0330 & 0.0413 \\ \cline{1-1}  & DualGNN & 0.0588 & 0.0899 & 0.0324 & 0.0404 \\ \cline{1-1}  & SLMRec & 0.0663 & 0.0990 & 0.0335 & 0.0421 \\ \cline{1-1}  & LATTICE & 0.0620 & 0.0953 & 0.0335 & 0.0421 \\ \cline{1-1}  & FREEDOM & 0.0717 & 0.1089 & 0.0385 & 0.0481 \\ \cline{1-1}  & UPGCN & 0.0740 & 0.1114 & 0.0402 & 0.0498 \\ \cline{1-1}  & Improv. (\%) & 3.21 & 2.29 & 4.41 & 3.53 \\ \hline \multirow{8}{*}{Clothing} & BPR & 0.0206 & 0.0303 & 0.0114 & 0.0138 \\  & LightGCN & 0.0361 & 0.0544 & 0.0197 & 0.0243 \\ \cline{1-1} \cline{2-6}  & VBPR & 0.0281 & 0.0415 & 0.0158 & 0.0192 \\ \cline{1-1}  & MMGCN & 0.0227 & 0.0361 & 0.0120 & 0.0154 \\ \cline{1-1}  & GRCN & 0.0421 & 0.0657 & 0.0224 & 0.0284 \\ \cline{1-1}  & DualGNN & 0.0452 & 0.0675 & 0.0242 & 0.0298 \\ \cline{1-1}  & SLMRec & 0.0442 & 0.0659 & 0.0241 & 0.0296 \\ \cline{1-1}  & LATTICE & 0.0492 & 0.0733 & 0.0268 & 0.0330 \\ \cline{1-1}  & FREEDOM & 0.0629 & 0.0941 & 0.0341 & 0.0420 \\ \cline{1-1}  & UPGCN & 0.0644 & 0.0959 & 0.0348 & 0.0428 \\ \cline{1-1}  & Improv. (\%) & 2.38 & 1.91 & 2.05 & 1.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of our UPGCN with baseline models (adapted from ref. [25]). Best results are underlined. The line below LightGCN serves as the boundary between the two types of methods mentioned in Section 4.2. The UPGCN model outperforms the presented models in terms of recommendation performance on each dataset. Specifically, UPGCN improves FREEDOM by 4.31%, 3.21%, and 2.38% in terms of R@10 on Baby, Sports, and Clothing, respectively. This indicates that our proposed model is well-designed for the multimodal recommendation. Specifically, to capture users' preference information for different modalities, we adopted a user-perception-guided representation enhancement module to obtain more accurate user representations. Additionally, by using a multimodal two-step enhanced fusion method, we separated the similarity between different modalities and highlighted the features of a single modality, thus avoiding contamination from modality noise and fully utilizing their complementarity to further enrich the item representation. The integration of multimodal information into the recommendation system has substantially improved the model's effectiveness in generating recommendations. For example, VBPR, which utilizes visual information of items, achieves a 15.3%, 31.1%, and 36.9% performance improvement in terms of R@20 compared to BPR on the Baby, Sports, and Clothing datasets, respectively. Furthermore, graph-based models (e.g., LightGCN) have also achieved improved recommendation performance by combining multimodal information.