# GC4 MRec: Generative-Contrastive for Multimodal Recommendation
## 5 Conclusions  
In this work, we proposed GC4 MRec, a novel self-supervised learning framework for multimodal recommendation. GC4 MRec leverages contrastive learning with a Variational Graph Autoencoder (VGAE) for generating augmented subgraphs, enabling the accurate learning of latent features and high-order user-item connectivity. This approach addresses a key limitation of existing contrastive learning methods in recommendation systems: the difficulty in defining effective pretext tasks and the sparsity of interaction data. The augmented subgraphs provide rich positive sample pairs, enhancing the model's ability to capture latent user preferences. Furthermore, a novel bidirectional cross-temporal residual module effectively fuses information from both original and augmented subgraphs, capturing dynamic shifts in user preferences across different temporal contexts, a critical aspect often overlooked by static fusion strategies. The introduction of a cross-channel fusion convolution module (CCFC) allows for the dynamic learning of metadata weights from  
Figure 6: Ablation experiment.  
different modalities, enabling more effective and personalized multimodal information fusion compared to simpler methods like concatenation or static weighted averaging.  
Extensive experiments on three public datasets demonstrate that GC4 MRec outperforms state-of-the-art models. These improvements are not just marginal but reflect GC4 MRec's ability to effectively model complex multimodal interactions and dynamic user preferences. For instance, the significant improvement on the Sports dataset in Recall@10 illustrates GC4 MRec's effectiveness in capturing long-tail user preferences and recommending relevant niche items, a direct result of the metadata-aware fusion mechanism and graph refinement. Ablation studies further validate the contributions of each component (the VGAE-based augmentation, the bidirectional residual module, and the CCFC fusion strategy), showcasing their synergistic effect in achieving superior recommendation accuracy.  
The implications of GC4 MRec extend beyond the datasets explored here. Its potential is applicable in various recommendation scenarios, such as social media feeds, news aggregation platforms, and e-commerce product suggestions, where diverse modalities and dynamic user behaviors are prevalent. Future work will focus on enriching data diversity by incorporating additional modalities, such as user reviews or product descriptions. This will provide a more holistic view of user preferences and item characteristics. Moreover, we will investigate the integration of large language models (LLMs). Specifically, we envision employing LLMs for generating synthetic user profiles or augmenting item descriptions, addressing the cold-start problem and enhancing the semantic representation of items. We also plan to explore personalized prompt engineering techniques for LLMs tailored to individual user preferences and item characteristics. This will enable more fine-grained control over the generation process and lead to even more accurate and personalized recommendations. By addressing these challenges and exploring these promising directions, GC4 MRec provides a robust framework and paves the way for the next generation of multimodal recommendation systems.  
Conceptualization: Y.L. and L.W.; Methodology: Y.L.; Software: L.W.; Validation: L.W., J.L. and H.W.; Formal Analysis: H.W.; Investigation: L.W.; Resources: L.W.; Data Curation: Y.L.; Writing--Original Draft Preparation: Y.L.; Writing--Review and Editing: Y.L.; Visualization: H.W.; Supervision: J.L.; Project Administration: J.L.; Funding Acquisition: L.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No.12271201).  
Not applicable.  
The original contributions presented in this study are included in the article, and further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.