摘要： 在互联网技术迅猛发展的当下，个性化推荐系统对于帮助用户筛选感兴趣内容扮演着至关重要的角色。传统的推荐方法在处理大规模数据和捕捉用户复杂偏好方面存在局限性，而现有的基于图神经网络（GNN）的推荐方法主要侧重于挖掘用户与物品间的直接交互关系，虽然提高了推荐的准确性，但其往往忽略了如文本、图像、音视频等多模态信息的融合与利用。元路径作为异构图中描述节点间复合关系的概念，有助于进一步提升嵌入质量和推荐效果，但现有模型要么忽略节点内容特征，要么丢弃了元路径上的中间节点，又或者仅考虑单一的元路径。针对现有多模态推荐系统的挑战，提出了一种基于元路径引导的多模态推荐方法（MAMGNN）。首先通过构建多模态异构图，整合来自不同模态的信息，然后利用元路径来引导信息在同种元路径内及不同种元路径间进行传播和聚合。此外，此方法还引入了图神经网络和注意力机制，以学习用户和物品的高质量嵌入表示，从而生成更精确且具有可解释性的推荐结果。在MovieLens-20M和H&M两个真实世界数据集上的广泛实验及小范围内的用户调研表明，MAMGNN在预测用户对项目的偏好程度方面效果显著提升，相较于基线模型，在Precision@10、Recall@10 和 NDCG@10三个指标上分别提高了约2.93%、1.98%、2.12%和3.43%、1.18%、2.40%。

Abstract: With the rapid development of Internet technology, personalized recommendation systems play a crucial role in helping users filter content of interest. Traditional recommendation methods have limitations in processing large-scale data and capturing users' complex preferences. Existing recommendation methods based on Graph Neural Networks (GNN) primarily focus on mining direct interactions between users and items. Although they improve the accuracy of recommendations, they often ignore the integration and utilization of multimodal information such as text, images, audio and video. Metapath, as a concept describing the composite relationship between nodes in heterogeneous graphs, can further improve the embedding quality and recommendation effect. However, existing models either ignore node content features, discard intermediate nodes on metapath, or only consider a single metapath. To address the challenges of existing multimodal recommendation systems, this study proposes a multimodal recommendation algorithm based on meta-path guidance (MAMGNN). Firstly, it constructs a multimodal heterogeneous information network to integrate information from different modalities, and then uses meta-paths to guide the propagation and aggregation of information within the intra-metapath and inter-metapath. Furthermore, it introduces Graph Neural Networks and attention mechanisms to learn high-quality embedding representations of users and items, thereby generating more accurate and explainable recommendation results. Extensive experiments on two real-world datasets, MovieLens-20M and H&M, and a small-scale user survey demonstrate that MAMGNN significantly enhances the performance in predicting users' preferences for items, outperforming baseline models in Precision@10, Recall@10, and NDCG@10 metrics by approximately 2.93%, 1.98%, 2.12%, and 3.43%, 1.18%, 2.40% respectively.