With the capacity to capture high-order collaborative signals, Graph Neural Networks (GNNs) have emerged as powerful methods in Recommender Systems (RS). However, their efficacy often hinges on the assumption that training and testing data share the same distribution (a.k.a. IID assumption), and exhibits significant declines under distribution shifts. Distribution shifts commonly arises in RS, often attributed to the dynamic nature of user preferences or ubiquitous biases during data collection in RS. Despite its significance, researches on GNN-based recommendation against distribution shift are still sparse. To bridge this gap, we propose Distributionally Robust GNN (DR-GNN) that incorporates Distributional Robust Optimization (DRO) into the GNN-based recommendation. DR-GNN addresses two core challenges: 1) To enable DRO to cater to graph data intertwined with GNN, we reinterpret GNN as a graph smoothing regularizer, thereby facilitating the nuanced application of DRO; 2) Given the typically sparse nature of recommendation data, which might impede robust optimization, we introduce slight perturbations in the training distribution to expand its support. Notably, while DR-GNN involves complex optimization, it can be implemented easily and efficiently. Our extensive experiments validate the effectiveness of DR-GNN against three typical distribution shifts. The code is available at https://github.com/WANGBohaO-jpg/DR-GNN.

翻译：凭借捕捉高阶协同信号的能力，图神经网络（GNNs）已成为推荐系统中的强大方法。然而，其有效性通常依赖于训练数据与测试数据服从相同分布的假设（即独立同分布假设），且在分布偏移下性能显著下降。推荐系统中的分布偏移普遍存在，常由用户偏好的动态变化或数据采集过程中的普遍偏差导致。尽管其重要性，针对分布偏移的基于GNN的推荐研究仍较为匮乏。为填补这一空白，我们提出分布鲁棒图神经网络（DR-GNN），将分布鲁棒优化（DRO）融入基于GNN的推荐系统。DR-GNN解决了两个核心挑战：1）为使DRO能够适用于与GNN交织的图数据，我们将GNN重新解释为图平滑正则化器，从而促进DRO的精细应用；2）鉴于推荐数据通常稀疏的特性可能阻碍鲁棒优化，我们在训练分布中引入微小扰动以扩展其支撑集。值得注意的是，尽管DR-GNN涉及复杂优化，但其实现简便高效。大量实验验证了DR-GNN在三种典型分布偏移下的有效性。代码见https://github.com/WANGBohaO-jpg/DR-GNN。