Multimodal Recommendation via Modality-Shared Encoding and Multi-Dimensional Loss Optimization

Multimodal embeddings of items and users, along with the loss function in the prediction model, are crucial for multimodal recommendation. Previous studies focused on feature extraction and fusion across modalities but lacked collaborative optimization of modal features across different association graphs. While prediction models address data alignment and user preference enhancement, they overlook preference consistency and varying contributions of different modalities to recommendations. In light of these issues, this paper proposes a multimodal recommendation method that combines Modality Shared Encoding with Multidimensional Loss optimization (MSE-ML). We introduce a multi-view feature joint encoding module, leveraging modality sharing mechanism to enable collaborative optimization of item embeddings across the user item graph and multiple modality-aware item association graphs. Additionally, we design a multidimensional loss optimization strategy that simultaneously promotes modality alignment, preserves preference consistency, and enhance the contribution of weak modality. Experimental results on publicly available datasets show that MSE-ML outperforms state-of-the-art approaches in terms of recommendation quality.