Signed Proximity Matters in Graph-based Recommendation

Graph-based models are a powerful technique for recommendation systems, which seek to leverage the graph structure created by user-item interactions for elevated performance. The majority of them are designed for unsigned graphs, which fail to exploit negative interactions (e.g., dislikes, returns) from users, and hence, incur compromised effectiveness. To tap into such negative signals, in recent years, a number of efforts have been invested towards extending graph neural networks (GNNs) and Transformer models to signed graphs. Unfortunately, the former approaches produce sub-par results due to the lack of access to global information, whereas the latter achieve superior performance for recommendation but suffer from severe over-globalizing problems and substantial computational overhead. To bridge this gap, this paper presents SPGNN, which significantly unleashes the capabilities of GNNs and advances its performance for top-K recommendation in signed graphs through two non-trivial technical contributions. Firstly, we propose to upgrade the neighborhood aggregation scheme in GNNs with two novel notions of signed local proximity (SLP) and signed global proximity (SGP) based on weak balance theory, which can accurately capture sign-aware multi-scale relations between nodes in signed graphs. On top of that, SPGNN includes a theoretically-grounded module for effective feature initialization, which carefully crafts sign-aware structure embeddings via fast spectral decomposition. Extensive experiments show that SPGNN significantly outperforms other unsigned and sign-aware models on six benchmark datasets with up to a gain of 19.42% in Recall and 28.18% in NDCG, which indicates the traditional GNN architecture also holds great potential for signed graph recommendation with appropriate modification. Our code is available at https://github.com/yfsong00/SPGNN.