Data subsampling is widely used to speed up the training of large-scale recommendation systems. Most subsampling methods are model-based and often require a pre-trained pilot model to measure data importance via e.g. sample hardness. However, when the pilot model is misspecified, model-based subsampling methods deteriorate. Since model misspecification is persistent in real recommendation systems, we instead propose model-agnostic data subsampling methods by only exploring input data structure represented by graphs. Specifically, we study the topology of the user-item graph to estimate the importance of each user-item interaction (an edge in the user-item graph) via graph conductance, followed by a propagation step on the network to smooth out the estimated importance value. Since our proposed method is model-agnostic, we can marry the merits of both model-agnostic and model-based subsampling methods. Empirically, we show that combing the two consistently improves over any single method on the used datasets. Experimental results on KuaiRec and MIND datasets demonstrate that our proposed methods achieve superior results compared to baseline approaches.

翻译：数据子采样广泛应用于加速大规模推荐系统的训练。大多数子采样方法依赖于模型，通常需要预训练的引导模型通过样本难度等指标衡量数据重要性。然而，当引导模型设定错误时，基于模型的子采样方法性能会下降。由于模型设定错误在真实推荐系统中普遍存在，我们转而提出仅通过探索由图表示的数据结构来实现模型无关的数据子采样方法。具体而言，我们研究用户-物品图的拓扑结构，通过图传导性估计每个用户-物品交互（即用户-物品图中的边）的重要性，随后在网络中进行传播步骤以平滑估计的重要性值。由于所提方法具有模型无关性，我们可以融合模型无关与基于模型的子采样方法的优势。实验表明，在所使用的数据集上，将两者结合始终优于任何单一方法。在KuaiRec和MIND数据集上的实验结果显示，我们的方法相较于基线方法取得了更优的结果。