# GC4 MRec: Generative-Contrastive for Multimodal Recommendation
###### Abstract  
The rapid growth of information technology has led to an explosion of data, posing a significant challenge for data processing. Recommendation systems aim to address this by providing personalized content recommendations to users from vast datasets. Recently, multimodal recommendation systems have gained considerable attention due to their ability to leverage diverse data modalities (e.g., images and text) for more accurate recommendations. However, effectively fusing these modalities to accurately represent user preferences remains a challenging task, despite progress made by existing multimodal recommendation approaches. To address this challenge, we propose a novel method which we call GC4 MRec (Generative-Contrastive for Multimodal Recommendation). On the one hand, we design a bilateral information flow module using two graph convolutional networks (GCNs). This module captures modal features from two distinct perspectives--standard and generatively augmented--to extract latent preferences. On the other hand, we introduce a novel modality fusion module that dynamically represents user multimodal fusion preferences, enabling the construction of accurate user preference profiles. Finally, we evaluate our proposed method, GC4 MRec, on three public real-world datasets and demonstrate its effectiveness compared to the state-of-the-art methods. multimodal recommendation; generative models; contrastive learning; graph convolutional network; multimodal fusion +
Footnote †: journal: Applied sciences  
19 January 2025  
Revised: 22 March 2025  
Accepted: 25 March 2025  
Published: 27 March 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]. interaction graph, propagating information through graph convolutional layers [9]. They capture higher-order connectivity between nodes by aggregating and propagating information across nodes with interactions, enabling the recommendation of items of interest to users [10; 11]. However, conventional deep learning-based recommendation systems fail to leverage the modality diversity of user interaction data. In real-world scenarios, user preferences can be reflected by data from different modalities, which exhibit user data complementarity. The proper fusion of these modalities can construct a comprehensive user profile, enabling the more accurate capture of user needs compared to single-modal recommendation systems. Existing works have combined multimodal data with GCNs, constructing independent channels to convolute and propagate user preference information for different modalities [12; 13]. DGVAE [14] enhances the interpretability of multimodal recommendations by unifying the mapping the data of image and text modalities into text, performing graph encoding and reconstruction separately. Deconstructing graph nodes to incorporate auxiliary information can enhance the performance of recommender systems [15; 16]. GCNs are particularly well suited for processing multimodal data due to their inherent ability to model complex relationships [17; 18]. For instance, MMGCN [12] offers a novel approach to personalized micro-video recommendation. Its strength lies in explicitly modeling user preferences across modalities, leveraging graph convolutional networks for representation refinement through information exchange on modality-aware user-item graphs. FREEDOM [19] addresses multimodal recommendation by freezing the item-item graph and denoising the user-item graph via degree-sensitive pruning. This approach achieves state-of-the-art performance with significantly reduced computational cost. LATTICE [13] addresses multimedia recommendation by learning latent item-item structures from multimodal features using a novel modality-aware structure learning layer and graph convolutions. However, some methods employ early-stage modality fusion techniques, such as simple weighted fusion or concatenation, lacking the ability to deeply explore the underlying preferences of user modalities. When the number of convolutional layers in GCNs increases, over-smoothing issues arise.