  Multimodal recommendation systems (MMRS) have received considerable attention
from the research community due to their ability to jointly utilize information
from user behavior and product images and text. Previous research has two main
issues. First, many long-tail items in recommendation systems have limited
interaction data, making it difficult to learn comprehensive and informative
representations. However, past MMRS studies have overlooked this issue.
Secondly, users' modality preferences are crucial to their behavior. However,
previous research has primarily focused on learning item modality
representations, while user modality representations have remained relatively
simplistic.To address these challenges, we propose a novel Graphs and User
Modalities Enhancement (GUME) for long-tail multimodal recommendation.
Specifically, we first enhance the user-item graph using multimodal similarity
between items. This improves the connectivity of long-tail items and helps them
learn high-quality representations through graph propagation. Then, we
construct two types of user modalities: explicit interaction features and
extended interest features. By using the user modality enhancement strategy to
maximize mutual information between these two features, we improve the
generalization ability of user modality representations. Additionally, we
design an alignment strategy for modality data to remove noise from both
internal and external perspectives. Extensive experiments on four publicly
available datasets demonstrate the effectiveness of our approach.
