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 …

Personalized recommendation plays a central role in various online applications. To provide
quality recommendation service, it is of crucial importance to consider multi-modal …

Most existing recommender systems represent a user's preference with a feature vector,
which is assumed to be fixed when predicting this user's preferences for different items …

Multimedia recommendation aims to predict user preferences where users interact with
multimodal items. Collaborative filtering based on graph convolutional networks manifests …

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

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

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 …

The recommender system has recently drawn a lot of attention to the communities of
information services and mobile applications. Many deep learning-based recommendation …

We address the multimedia recommendation problem, which utilizes items' multimodal
features, such as visual and textual modalities, in addition to interaction information. While a …

In recent years, the rapid growth of online multimedia services, such as e-commerce
platforms, has necessitated the development of personalised recommendation approaches …