Multimodal Recommendation

The task of multimodal recommendation involves developing systems capable of integrating and utilizing multiple types of data, such as text, images, audio, and user interactions, to predict and suggest items that align with user preferences. This task aims to enhance the richness and intricacy of user and item representations by fusing heterogeneous data, capturing complex relationships and attributes across different data types, thereby improving the accuracy and relevance of recommendations. The core objective is to effectively process multi-source information to achieve personalized recommendations and enhance user experience.