Recommender systems now consume large-scale data and play a significant role in improving user experience. Graph Neural Networks (GNNs) have emerged as one of the most effective recommender system models because they model the rich relational information. The ever-growing volume of data can make training GNNs prohibitively expensive. To address this, previous attempts propose to train the GNN models incrementally as new data blocks arrive. Feature and structure knowledge distillation techniques have been explored to allow the GNN model to train in a fast incremental fashion while alleviating the catastrophic forgetting problem. However, preserving the same amount of the historical information for all users is sub-optimal since it fails to take into account the dynamics of each user's change of preferences. For the users whose interests shift substantially, retaining too much of the old knowledge can overly constrain the model, preventing it from quickly adapting to the users' novel interests. In contrast, for users who have static preferences, model performance can benefit greatly from preserving as much of the user's long-term preferences as possible. In this work, we propose a novel training strategy that adaptively learns personalized imitation weights for each user to balance the contribution from the recent data and the amount of knowledge to be distilled from previous time periods. We demonstrate the effectiveness of learning imitation weights via a comparison on five diverse datasets for three state-of-art structure distillation based recommender systems. The performance shows consistent improvement over competitive incremental learning techninques.

翻译：推荐系统如今处理大规模数据，在提升用户体验方面发挥着重要作用。图神经网络因其能够建模丰富的关联信息，已成为最有效的推荐系统模型之一。然而，数据量的持续增长使得训练图神经网络的成本极为高昂。为此，先前的研究提出在新数据块到达时增量式地训练图神经网络模型。特征与结构知识蒸馏技术被探索用于使图神经网络以快速增量方式训练，同时缓解灾难性遗忘问题。然而，为所有用户保留相同数量的历史信息并非最优策略，因为它未能考虑每个用户偏好变化的动态性。对于兴趣发生显著变化的用户，保留过多旧知识会过度约束模型，阻碍其快速适应用户的新兴趣。相反，对于偏好稳定的用户，尽可能保留用户的长期偏好可以显著提升模型性能。在本工作中，我们提出一种新的训练策略，该策略为每位用户自适应地学习个性化模仿权重，以平衡来自近期数据的贡献与从之前时间段蒸馏的知识量。我们通过五个不同数据集上三种基于结构蒸馏的最先进推荐系统的对比，验证了学习模仿权重的有效性。性能表现显示，该方法相较于有竞争力的增量学习技术具有持续改进。