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.

翻译：多模态推荐系统（MMRS）因其能够联合利用用户行为与商品图像、文本信息的能力而受到研究界的广泛关注。现有研究存在两个主要问题：首先，推荐系统中许多长尾商品交互数据有限，难以学习全面且信息丰富的表征，而过往的MMRS研究忽视了这一问题；其次，用户对模态的偏好对其行为至关重要，但先前研究主要集中于学习商品模态表征，而用户模态表征仍相对简单。为应对这些挑战，我们提出一种面向长尾多模态推荐的新型图结构与用户模态增强方法（GUME）。具体而言，我们首先利用商品间的多模态相似性增强用户-商品图，这提升了长尾商品的连通性，并帮助它们通过图传播学习高质量表征。随后，我们构建两种类型的用户模态：显式交互特征与扩展兴趣特征。通过采用用户模态增强策略最大化这两个特征间的互信息，我们提升了用户模态表征的泛化能力。此外，我们设计了模态数据的对齐策略，从内部和外部两个角度消除噪声。在四个公开数据集上的大量实验证明了我们方法的有效性。