  In recent years, many recommender systems using network embedding (NE) such
as graph neural networks (GNNs) have been extensively studied in the sense of
improving recommendation accuracy. However, such attempts have focused mostly
on utilizing only the information of positive user-item interactions with high
ratings. Thus, there is a challenge on how to make use of low rating scores for
representing users' preferences since low ratings can be still informative in
designing NE-based recommender systems. In this study, we present SiReN, a new
sign-aware recommender system based on GNN models. Specifically, SiReN has
three key components: 1) constructing a signed bipartite graph for more
precisely representing users' preferences, which is split into two
edge-disjoint graphs with positive and negative edges each, 2) generating two
embeddings for the partitioned graphs with positive and negative edges via a
GNN model and a multi-layer perceptron (MLP), respectively, and then using an
attention model to obtain the final embeddings, and 3) establishing a
sign-aware Bayesian personalized ranking (BPR) loss function in the process of
optimization. Through comprehensive experiments, we empirically demonstrate
that SiReN consistently outperforms state-of-the-art NE-aided recommendation
methods.
