  Recommender systems play a crucial role in addressing the issue of
information overload by delivering personalized recommendations to users. In
recent years, there has been a growing interest in leveraging graph neural
networks (GNNs) for recommender systems, capitalizing on advancements in graph
representation learning. These GNN-based models primarily focus on analyzing
users' positive feedback while overlooking the valuable insights provided by
their negative feedback. In this paper, we propose PANE-GNN, an innovative
recommendation model that unifies Positive And Negative Edges in Graph Neural
Networks for recommendation. By incorporating user preferences and
dispreferences, our approach enhances the capability of recommender systems to
offer personalized suggestions. PANE-GNN first partitions the raw rating graph
into two distinct bipartite graphs based on positive and negative feedback.
Subsequently, we employ two separate embeddings, the interest embedding and the
disinterest embedding, to capture users' likes and dislikes, respectively. To
facilitate effective information propagation, we design distinct
message-passing mechanisms for positive and negative feedback. Furthermore, we
introduce a distortion to the negative graph, which exclusively consists of
negative feedback edges, for contrastive training. This distortion plays a
crucial role in effectively denoising the negative feedback. The experimental
results provide compelling evidence that PANE-GNN surpasses the existing
state-of-the-art benchmark methods across four real-world datasets. These
datasets include three commonly used recommender system datasets and one
open-source short video recommendation dataset.
