In signed social recommendation, while modeling trust and distrust relations shows great potential, its effectiveness is often hindered by structural noise and data sparsity.
We first identify a fundamental inconsistency across the structural, propagation, and semantic layers of existing models, leading to biased representations learned from sparse or noisy datasets.
Furthermore, we observe that most existing methods treat the observed graph as fixed, failing to bridge the gap between noisy topologies and reliable social semantics.
To address these issues, we propose a unified framework called SSC-Loop that treats signed social recommendation as the maximization of structural consistency. SSC-Loop includes three dedicated modules: ESA-DA for structural consistency, a P/N/O propagation mechanism for propagation consistency, and a contrastive learning objective for semantic consistency.
Experiments on Epinions demonstrate that SSC-Loop achieves strong performance on explicit signed social rating prediction, while auxiliary results on Slashdot further suggest its ability to exploit signed social structures. Source code is available at GitHub.
Blogger's Review: The SSC-Loop framework proposed in this study effectively addresses the shortcomings of existing models in handling noise and sparse data by maximizing structural consistency, showcasing the broad application potential of signed social recommendation. Its modular design provides a solid foundation for further research and practical implementation.