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 this https URL

本研究聚焦于使用图卷积网络（GCNs）的多媒体推荐系统，旨在更有效地利用多模态特征以准确捕捉用户对项目的偏好。通过提出模态包容 GCN（MeGCN）和目标感知注意力等两个核心思想，我们的实验表明 MONET 在七种最先进的竞争者中非常优越（在召回率 @20 方面比最佳竞争者高出 30.32％），并证明了 MONET 的两个核心思想的有效性。

MONET: 多模态图卷积网络和目标感知注意力在多媒体推荐中的应用

MONET: Modality-Embracing Graph Convolutional Network and Target-Aware  Attention for Multimedia Recommendation

Multimedia recommender systems suggest media items, e.g., songs, (digital)
books and movies, to users by utilizing concepts of traditional recommender
systems such as collaborative filtering. In this paper, we investigate a
potential issue of such collaborative-filtering based multimedia recommender
systems, namely popularity bias that leads to the underrepresentation of
unpopular items in the recommendation lists. Therefore, we study four
multimedia datasets, i.e., LastFm, MovieLens, BookCrossing and MyAnimeList,
that we each split into three user groups differing in their inclination to
popularity, i.e., LowPop, MedPop and HighPop. Using these user groups, we
evaluate four collaborative filtering-based algorithms with respect to
popularity bias on the item and the user level. Our findings are three-fold:
firstly, we show that users with little interest into popular items tend to
have large user profiles and thus, are important data sources for multimedia
recommender systems. Secondly, we find that popular items are recommended more
frequently than unpopular ones. Thirdly, we find that users with little
interest into popular items receive significantly worse recommendations than
users with medium or high interest into popularity.

研究多媒体推荐系统的协作过滤算法，并发现其中的流行度偏差问题，导致推荐列表中较不受欢迎的项目被低估。通过对 LastFm、MovieLens、BookCrossing 和 MyAnimeList 等数据集进行研究，结果表明对普及度不高的项目感兴趣的用户的推荐体验要比对普及度中等和高的项目感兴趣的用户的推荐体验更差。