User interaction data in recommender systems is a form of dyadic relation that reflects the preferences of users with items. Learning the representations of these two discrete sets of objects, users and items, is critical for recommendation. Recent multimodal recommendation models leveraging multimodal features (e.g., images and text descriptions) have been demonstrated to be effective in improving recommendation accuracy. However, state-of-the-art models enhance the dyadic relations between users and items by considering either user-user or item-item relations, leaving the high-order relations of the other side (i.e., users or items) unexplored. Furthermore, we experimentally reveal that the current multimodality fusion methods in the state-of-the-art models may degrade their recommendation performance. That is, without tainting the model architectures, these models can achieve even better recommendation accuracy with uni-modal information. On top of the finding, we propose a model that enhances the dyadic relations by learning Dual RepresentAtions of both users and items via constructing homogeneous Graphs for multimOdal recommeNdation. We name our model as DRAGON. Specifically, DRAGON constructs the user-user graph based on the commonly interacted items and the item-item graph from item multimodal features. It then utilizes graph learning on both the user-item heterogeneous graph and the homogeneous graphs (user-user and item-item) to obtain the dual representations of users and items. To capture information from each modality, DRAGON employs a simple yet effective fusion method, attentive concatenation, to derive the representations of users and items. Extensive experiments on three public datasets and seven baselines show that DRAGON can outperform the strongest baseline by 22.03% on average. Various ablation studies are conducted on DRAGON to validate its effectiveness.
翻译:推荐者系统中的用户互动数据是反映用户对项目的偏好的一种三角关系形式。学习这两套离散的物件、用户和物品的表述方式对建议至关重要。最近利用多式联运特点的多式联运建议模式(例如图像和文本说明)已证明对提高建议准确性十分有效。然而,最先进的模型通过考虑用户用户-用户或项目项目之间的关系,加强了用户与项目之间的双向关系,使另一方(即用户或项目)的高度顺序关系得以解析。此外,我们实验性地发现,目前最先进的对象、用户和物品的混合方法可能会降低其建议性能。在不玷污模型结构的情况下,这些模型可以提高建议对单式信息的准确性。在调查中,我们提出一个模型,通过学习用户和用户的双向正态(即为多式Odal 重新分类)的直径直径直图和多式图式图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图和图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型图型