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：数据子采样被广泛用于加速大规模推荐系统的训练过程。大多数子采样方法基于模型，通常需要预训练的引导模型通过样本难度等方式评估数据重要性。然而，当引导模型设定不当时，基于模型的子采样方法性能会下降。由于真实推荐系统中模型设定偏差普遍存在，我们转而提出仅通过探索图所表示的输入数据结构的模型无关数据子采样方法。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（用户-物品图中的边）的重要性，随后在网络上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，在所用数据集上，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证明，与基线方法相比，我们提出的方法取得了更优的结果。