The multi-criteria (MC) recommender system, which leverages MC rating information in a wide range of e-commerce areas, is ubiquitous nowadays. Surprisingly, although graph neural networks (GNNs) have been widely applied to develop various recommender systems due to GNN's high expressive capability in learning graph representations, it has been still unexplored how to design MC recommender systems with GNNs. In light of this, we make the first attempt towards designing a GNN-aided MC recommender system. Specifically, rather than straightforwardly adopting existing GNN-based recommendation methods, we devise a novel criteria preference-aware light graph convolution CPA-LGC method, which is capable of precisely capturing the criteria preference of users as well as the collaborative signal in complex high-order connectivities. To this end, we first construct an MC expansion graph that transforms user--item MC ratings into an expanded bipartite graph to potentially learn from the collaborative signal in MC ratings. Next, to strengthen the capability of criteria preference awareness, CPA-LGC incorporates newly characterized embeddings, including user-specific criteria-preference embeddings and item-specific criterion embeddings, into our graph convolution model. Through comprehensive evaluations using four real-world datasets, we demonstrate (a) the superiority over benchmark MC recommendation methods and benchmark recommendation methods using GNNs with tremendous gains, (b) the effectiveness of core components in CPA-LGC, and (c) the computational efficiency.

翻译：多准则推荐系统广泛利用电子商务领域中的多准则评分信息，如今已普及应用。值得注意的是，尽管图神经网络因其在图表示学习中的强大表达能力已被广泛用于开发各类推荐系统，但如何设计基于图神经网络的多准则推荐系统仍是未解难题。为此，我们首次尝试设计一种图神经网络辅助的多准则推荐系统。具体而言，我们并未直接采用现有的图神经网络推荐方法，而是提出了一种新颖的准则偏好感知轻量图卷积方法CPA-LGC，该方法既能精准捕捉用户的准则偏好，又能捕获复杂高阶连接中的协同信号。为实现这一目标，我们首先构建了一个多准则扩展图，将用户-物品的多准则评分转化为扩展二部图，以潜在地学习多准则评分中的协同信号。其次，为增强准则偏好感知能力，CPA-LGC将新表征的嵌入向量（包括用户特定准则偏好嵌入和物品特定准则嵌入）融入图卷积模型。通过使用四个真实数据集的综合评估，我们证明了：(a) 该方法相较于基准多准则推荐方法及使用图神经网络的基准推荐方法具有显著的性能优势；(b) CPA-LGC核心组件的有效性；(c) 其计算效率。