The field of recommender systems is moving towards leveraging multimodal data to improve recommendation accuracy and personalized user experience. Research is focusing on developing innovative architectures and methods that can effectively integrate and process multimodal information, such as text, images, and user behavior. Noteworthy papers in this area include PREMISE, which introduces a new architecture for matching-based learning in multimodal fields, and RAGAR, which proposes a retrieval-augmented approach for personalized image generation. Additionally, Tricolore presents a multi-behavior user profiling framework for enhanced candidate generation, while LIRDRec learns item representations directly from multimodal features. These advancements have the potential to significantly improve the performance and diversity of recommender systems.