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| Recommendation systems have become fundamental in e-commerce scenarios, and session-based recommendation plays an increasingly significant role in recommendation systems because of its flexibility and highly practical value. Although there have been some promising results in previous works, they are still insufficient to achieve superior recommendation performance due to the limited even noisy information involved in the next click in each session. To obtain more accurate predictive vectors without the misleading of potential noisy information, we propose Self-Distillation Graph Neural Networks to make full use of the valuable information in a session, which is termed as SD-GNN for brevity. Specifically, we employ the well-evaluated and flexible deep ensemble in deep learning as the teacher model, which assembles multiple randomly initialized GNNs in a simple way. Furthermore, we leverage the soft target distribution produced by the teacher model to train each GNN in the ensemble to achieve self-knowledge distillation. Our whole method is easily implementable and scalable due to the proposed Self-Distillation technique. Extensive experiments on two benchmark datasets verify that the proposed method (SD-GNN) significantly outperforms state-of-the-art baselines and shows powerful performance in the session-based recommendation. |
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| Keywords:Graph Neural Networks.; Session-based Recommendation ; Knowledge Distillation |
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