Graph neural networks (GNNs) have become crucial in multimodal recommendation tasks because of their powerful ability to capture complex relationships between neighboring nodes. However, increasing the number of propagation layers in GNNs can lead to feature redundancy, which may negatively impact the overall recommendation performance. In addition, the existing recommendation task method directly maps the preprocessed multimodal features to the low-dimensional space, which will bring the noise unrelated to user preference, thus affecting the representation ability of the model. To tackle the aforementioned challenges, we propose Multimodal Graph Neural Network for Recommendation (MGNM) with Dynamic De-redundancy and Modality-Guided Feature De-noisy, which is divided into local and global interaction. Initially, in the local interaction process,we integrate a dynamic de-redundancy (DDR) loss function which is achieved by utilizing the product of the feature coefficient matrix and the feature matrix as a penalization factor. It reduces the feature redundancy effects of multimodal and behavioral features caused by the stacking of multiple GNN layers. Subsequently, in the global interaction process, we developed modality-guided global feature purifiers for each modality to alleviate the impact of modality noise. It is a two-fold guiding mechanism eliminating modality features that are irrelevant to user preferences and captures complex relationships within the modality. Experimental results demonstrate that MGNM achieves superior performance on multimodal information denoising and removal of redundant information compared to the state-of-the-art methods.

翻译：图神经网络因其强大的捕获相邻节点间复杂关系的能力，已成为多模态推荐任务中的关键技术。然而，增加图神经网络的传播层数可能导致特征冗余，从而对整体推荐性能产生负面影响。此外，现有推荐方法直接将预处理后的多模态特征映射到低维空间，这会引入与用户偏好无关的噪声，进而影响模型的表征能力。为应对上述挑战，我们提出一种基于动态去冗余与模态引导特征去噪的多模态图神经网络推荐模型，该模型分为局部交互与全局交互两个阶段。首先，在局部交互过程中，我们引入动态去冗余损失函数，该函数通过将特征系数矩阵与特征矩阵的乘积作为惩罚因子来实现，有效减少了因多层图神经网络堆叠导致的多模态特征与行为特征冗余效应。随后，在全局交互过程中，我们为每个模态设计了模态引导的全局特征净化器，以减轻模态噪声的影响。该机制通过双重引导策略，剔除与用户偏好无关的模态特征，同时捕获模态内部的复杂关系。实验结果表明，相较于现有最优方法，所提模型在多模态信息去噪与冗余信息消除方面均取得了更优的性能。