Recommendation algorithms forecast user preferences by correlating user and item
representations derived from historical interaction patterns. In pursuit of enhanced …

Multimodal Recommendation focuses mainly on how to effectively integrate behavior and
multimodal information in the recommendation task. Previous works suffer from two major …

Many multimodal recommender systems have been proposed to exploit the rich side
information associated with users or items (eg, user reviews and item images) for learning …

With the increasing multimedia information, multimodal recommendation has received
extensive attention. It utilizes multimodal information to alleviate the data sparsity problem in …

Multimodal recommendation exploits the rich multimodal information associated with users
or items to enhance the representation learning for better performance. In these methods …

User-item interaction data in recommender systems is a form of dyadic relation, reflecting
user preferences for specific items. To generate accurate recommendations, it is crucial to …

Multimodal recommendation system has been widely used in short video platform, e-
commerce platform and news media. Multimodal data contains information such as product …

Multimodal recommendation systems are widely used in e-commerce and short video
platforms. Compared with basic item attribute information, users are more likely to be …

Multi-modal recommendation greatly enhances the performance of recommender systems
by modeling the auxiliary information from multi-modality contents. Most existing multi-modal …

Multimodal recommendation is an emerging task with the goal of improving the effectiveness
of the recommendation system by utilizing multimodal data (images, texts, etc.). Most …