# GC4 MRec: Generative-Contrastive for Multimodal Recommendation
## 4 Experiment
### Performance Comparison  
Table 2 presents the performance comparison of our proposed GC4 MRec method against several state-of-the-art baselines on three datasets. We observe the following key trends:  
1. **Effectiveness of Graph-Based and Multimodal Methods:** GCN-based methods consistently outperform MF-based models (MF-BPR and VBPR) across all datasets and metrics. This highlights the effectiveness of modeling user-item interactions as a graph and leveraging graph convolutional operations. Furthermore, incorporating multimodal information generally leads to improved performance. Multimodal methods like VBPR, LATTICE, FREEDOM, SLMRec, and MMSSL outperform MF-BPR in most cases. This demonstrates the value of leveraging visual and textual features for enriching item representations and improving recommendation quality. 2. **Superiority of GC4 MRec:** Our proposed GC4 MRec method achieves the best performance across all datasets and metrics. It surpasses the strongest baseline of DGVAE, with improvements on Sports, Baby, and Clothing. GC4 MRec achieves relative improvements of 4.12%, 6.45% and 4.20%, respectively, in terms of R@10 over DGVAE. These gains demonstrate the effectiveness of GC4 MRec. **Impact of Multimodal Features:** While multimodal methods generally perform better, the effectiveness of multimodal features can vary across datasets. For example, DualGNN significantly improves upon LightGCN on Clothing, where visual and textual information likely plays a more crucial role in characterizing items, but shows less pronounced gains on Sports and Baby. This suggests that the informativeness of multimodal features can be dataset-dependent. Benefits of Graph Structure Refinement: Methods that refine or augment the graph structure, such as FREEDOM (with its denoising and freezing components) and LATTICE (with its latent item-item graph), tend to perform well. FREEDOM consistently achieves strong results, highlighting the importance of mitigating noise in user-item interactions and leveraging robust item relationships. Comparison with Self-Supervised Methods: Self-supervised methods like SLMRec, BM3, and MMSSL demonstrate competitive performance, showcasing the potential of leveraging self-supervision for learning better representations. However, GC4 MRec still outperforms these methods. Through extensive experiments on three publicly available datasets, the proposed GC4 MRec model demonstrably surpasses state-of-the-art models, highlighting its effectiveness compared to existing self-supervised methods. Our work introduces a novel approach for enhancing subgraph representation through generative contrastive learning. By generating positive sample pairs from augmented subgraphs, the model effectively amplifies the representation of latent features within the data. Furthermore, a novel dual spatio-temporal cross-residual module is proposed, effectively fusing information from the original and augmented subgraphs, enabling the capture of dynamic user preference shifts across different temporal contexts. A key innovation lies in the channel cross fusion convolution strategy, dynamically learning weights for metadata across different modalities. This enables effective multimodal information fusion, leading to more accurate preference recommendations for users. These gains demonstrate the effectiveness of GC4 MRec. Its superiority is further corroborated by the performance curves depicted in Figure 5. GC4 MRec continues to improve with increasing epochs, eventually converging to significantly higher values for both metrics, indicating that GC4 MRec's advantage becomes more pronounced with extended training. This suggests that GC4 MRec is better able to leverage the available data and learn more nuanced representations over time. This visual analysis further underscores the effectiveness of our proposed bilateral information flow module, generative augmentation via VGAE, and metadata preference modeling in capturing latent user preferences for improving recommendation accuracy. The improvement across datasets