GC4MRec: Generative-Contrastive for Multimodal Recommendation

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 GC4MRec (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, GC4MRec, on three public real-world datasets and demonstrate its effectiveness compared to the state-of-the-art methods.