MONET：面向多媒体推荐的模态融合图卷积网络与目标感知注意力机制
MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation
AI总结：
针对现有基于GCN的多媒体推荐系统多模态特征利用不足、用户嵌入未结合目标物品模态的问题，提出含模态融合GCN与目标感知注意力的MONET模型，在四个真实数据集上性能显著优于SOTA方法。
摘要：
本文聚焦于结合多模态特征与用户-物品交互信息、基于图卷积网络（GCN）的多媒体推荐系统，旨在更有效地利用多模态特征，精准捕捉用户对物品的偏好。为此，我们指出现有基于GCN的多媒体推荐系统存在两点局限：（L1）尽管用户交互过的物品的多模态特征能够反映其物品偏好，但现有方法采用的GCN仅专注于捕捉协同信号，导致多模态特征在最终的用户/物品嵌入中体现不足；（L2）尽管用户是通过考量目标物品的多模态特征来决定是否偏好该物品，但现有方法无论目标物品的多模态特征如何，都将用户表示为单一嵌入，再用该嵌入预测用户对目标物品的偏好。为解决上述问题，我们提出一种名为MONET的新型多媒体推荐系统，其核心包含两大设计：模态融合GCN（MeGCN）和目标感知注意力机制。通过在四个真实世界数据集上开展的大量实验，我们证明：i）MONET相较于七种当前最优的竞品模型具有显著优势（与最优竞品相比，recall@20指标下的准确率最高提升30.32%）；ii）MONET的两大核心设计均具备有效性。MONET的全部代码可在https://github.com/Kimyungi/MONET获取。
英文摘要：
In this paper, we focus on multimedia recommender systems using graph convolutional networks (GCNs) where the multimodal features as well as user-item interactions are employed together. Our study aims to exploit multimodal features more effectively in order to accurately capture users' preferences for items. To this end, we point out following two limitations of existing GCN-based multimedia recommender systems: (L1) although multimodal features of interacted items by a user can reveal her preferences on items, existing methods utilize GCN designed to focus only on capturing collaborative signals, resulting in insufficient reflection of the multimodal features in the final user/item embeddings; (L2) although a user decides whether to prefer the target item by considering its multimodal features, existing methods represent her as only a single embedding regardless of the target item's multimodal features and then utilize her embedding to predict her preference for the target item. To address the above issues, we propose a novel multimedia recommender system, named MONET, composed of following two core ideas: modality-embracing GCN (MeGCN) and target-aware attention. Through extensive experiments using four real-world datasets, we demonstrate i) the significant superiority of MONET over seven state-of-the-art competitors (up to 30.32% higher accuracy in terms of recall@20, compared to the best competitor) and ii) the effectiveness of the two core ideas in MONET. All MONET codes are available at https://github.com/Kimyungi/MONET.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
