Multi-modal recommendation systems, which integrate diverse types of information, have
gained widespread attention in recent years. However, compared to traditional collaborative …

Sequential recommendation systems utilize the sequential interactions of users with items
as their main supervision signals in learning users' preferences. However, existing methods …

Within online platforms, it is critical to capture the dynamic user preference from the
sequential interaction behaviors for making accurate recommendation over time. Recently …

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

Beyond conventional recommendation systems that rely merely on user-item interaction
data, multimodal recommendation systems additionally exploit the item multimodal data for …

The goal of sequential recommendation (SR) is to predict a user's potential interested items
based on her/his historical interaction sequences. Most existing sequential recommenders …

In sequential recommendation, multi-modal information (eg, text or image) can provide a
more comprehensive view of an item's profile. The optimal stage (early or late) to fuse …

Sequential Recommendation (SR) characterizes evolving patterns of user behaviors by
modeling how users transit among items. However, the short interaction sequences limit the …

Multimedia contents are of predominance in the modern Web era. Recent years have
witnessed growing research interests in multimedia recommendation, which aims to predict …

The main idea of multimodal recommendation is the rational utilization of the item's
multimodal information to improve the recommendation performance. Previous works …