Neural Multi-network Diffusion towards Social Recommendation

Boxin Du, Lihui Liu, Jiejun Xu, Fei Wang, Hanghang Tong
4/11/2023
cs.LGcs.SI

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Abstract

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).

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Cite this paper

@article{du2023neural,
  title  = {Neural Multi-network Diffusion towards Social Recommendation},
  author = {Boxin Du and Lihui Liu and Jiejun Xu and Fei Wang and Hanghang Tong},
  year   = {2023},
  eprint = {2304.04994},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.04994v1}
}

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