  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.
