Graph neural networks, with their capacity to capture complex hierarchical relations, are
extensively employed in multi-modal recommendation. Previous graph-based multi-modal …

Recommendation services play a pivotal role in financial decision-making and multimedia
content services, as they suggest investment operations and personalized items to users …

Research on recommendation methods using multimodal graph information presents a
significant challenge within the realm of information services. Prior studies in this area have …

The multimodal recommendation has gradually become the infrastructure of online media
platforms, enabling them to provide personalized service to users through a joint modeling …

This article presents a novel approach to multimodal recommendation systems, focusing on
integrating and purifying multimodal data. Our methodology starts by developing a filter to …

Multimedia-based recommendation is a challenging task that requires not only learning
collaborative signals from user-item interaction, but also capturing modality-specific user …

The multimodal side information such as images and text have been commonly used as
supplements to improve graph collaborative filtering recommendations. However, there is …

In a multimedia recommender system, rich multimodal dynamics of user-item interactions
are worth availing ourselves of and have been facilitated by Graph Convolutional Networks …

Multi-modal recommendation aims to leverage multi-modal information for mining users'
latent preferences. Existing multi-modal recommendation approaches primarily exploit …

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 …