Recently, multimodal recommendations have gained increasing attention for effectively
addressing the data sparsity problem by incorporating modality-based representations …

Multimodal recommendation systems (MMRS) have received considerable attention from
the research community due to their ability to jointly utilize information from user behavior …

Multimodal recommender systems have acquired applications in broad web scenarios such
as e-commerce businesses and short-video platforms. Existing multimodal recommendation …

This paper studies the multi-modal recommendation problem, where the item multi-modality
information (eg, images and textual descriptions) is exploited to improve the …

The considerable semantic information contained in multimodal data is increasingly
appreciated by industry and academia. To effectively leverage multimodal information …

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 …

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

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

Multi-modal recommender systems (MRSs) are pivotal in diverse online web platforms and
have garnered considerable attention in recent years. However, previous studies overlook …

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