A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions

This survey reviews multimodal recommender systems, which are AI models that personalize user experiences by analyzing multiple types of data (such as text, images, and user behavior) together rather than separately. The researchers found that these systems can discover complementary information across different data types that single-type systems might miss, and they provide a framework for implementing and comparing these models.