The field of recommendation systems is moving towards incorporating multimodal data to improve performance. Researchers are exploring ways to effectively integrate diverse data types, such as text, images, and user behavior, to enhance recommendation accuracy. A key challenge is addressing the complexity of multimodal data, including missing or incomplete modalities, and developing methods to robustly model user preferences across different domains. Recent studies have proposed innovative solutions, including disentangled representation learning, invariant learning, and diffusion-based feature denoising, to tackle these challenges. Notably, some papers have demonstrated the effectiveness of these approaches in real-world applications, highlighting their potential to improve recommendation systems. Noteworthy papers include: M^2VAE, which proposes a generative model to address cold-start item recommendation by leveraging multi-modal content and user preferences. I$^3$-MRec, which introduces a novel method for incomplete modality recommendation using invariant learning and information bottleneck principle. Does Multimodality Improve Recommender Systems as Expected, which provides a critical analysis of multimodal recommendation systems and offers practical insights for building efficient and effective systems.