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

In multimodal-aware recommendation, the extraction of meaningful multimodal features is at
the basis of high-quality recommendations. Generally, each recommendation framework …

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

This paper presents an open-source toolbox, MMRec for multimodal recommendation.
MMRec simplifies and canonicalizes the process of implementing and comparing …

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

In recommender systems, the lack of interaction data between users and items tends to lead
to the problem of data sparsity and cold starts. Recently, the interest modeling frameworks …

Recommendation system plays an important role in the rapid development of micro-video
sharing platform. Micro-video has rich modal features, such as visual, audio, and text. It is of …

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

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 …

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 …