Neural Multi-network Diffusion towards Social Recommendation
This work addresses social recommendation for users, but it is incremental as it builds on existing GNN methods with specific enhancements.
The paper tackled the problems of generalization and oversmoothness in GNN-based social recommendation by proposing a multi-network GNN model with generative negative sampling, resulting in performance improvements of up to 38.8% in NDCG@15 on benchmark datasets.
Graph Neural Networks (GNNs) have been widely applied on a variety of real-world applications, such as social recommendation. However, existing GNN-based models on social recommendation suffer from serious problems of generalization and oversmoothness, because of the underexplored negative sampling method and the direct implanting of the off-the-shelf GNN models. In this paper, we propose a succinct multi-network GNN-based neural model (NeMo) for social recommendation. Compared with the existing methods, the proposed model explores a generative negative sampling strategy, and leverages both the positive and negative user-item interactions for users' interest propagation. The experiments show that NeMo outperforms the state-of-the-art baselines on various real-world benchmark datasets (e.g., by up to 38.8% in terms of NDCG@15).