  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.
