Correlation-Guided Information Deep Fusion for Multimodal Recommendation

Multimodal recommendation plays a crucial role on online platforms by integrating modalities information such as visual, textual, and audio, which significantly mitigates the sparsity of user-item interaction networks. However, current multimodal recommendation methods primarily enrich item-side representations while neglecting user-side learning. And the fusion of structure and information is insufficient. To address these issues, we propose Correlation-guided Information Deep Fusion for Multimodal Recommendation(CIDF). First, we employ graph neural networks to capture collaborative signals based on ID embeddings and multimodal features separately, thereby capturing the independent information of each node’s different representations. Next, we construct the user-user similarity ID graph and the item-item correlation modality graph to capture connection information on user and item sides, respectively. Finally, we propose an information deep fusion method. This method integrates the aforementioned two graphs and the user-item interaction graph, thereby obtaining fused representations for both users and items through the process of information propagation and aggregation on graphs. The fused representations are further updated in user-item interaction graph to obtain node representations that better align with user interaction behaviors. We conducted experiments on real-world datasets, and the results demonstrate that CIDF outperforms state-of-the-art methods in multimodal recommendation. Our code is available at: https://github.com/Andrewsama/CIDF-master.