Graph neural networks (GNNs) have shown the power in representation learning over graph-structured user-item interaction data for collaborative filtering (CF) task. However, with their inherently recursive message propagation among neighboring nodes, existing GNN-based CF models may generate indistinguishable and inaccurate user (item) representations due to the over-smoothing and noise effect with low-pass Laplacian smoothing operators. In addition, the recursive information propagation with the stacked aggregators in the entire graph structures may result in poor scalability in practical applications. Motivated by these limitations, we propose a simple and effective collaborative filtering model (SimRec) that marries the power of knowledge distillation and contrastive learning. In SimRec, adaptive transferring knowledge is enabled between the teacher GNN model and a lightweight student network, to not only preserve the global collaborative signals, but also address the over-smoothing issue with representation recalibration. Empirical results on public datasets show that SimRec archives better efficiency while maintaining superior recommendation performance compared with various strong baselines. Our implementations are publicly available at: https://github.com/HKUDS/SimRec.

翻译：图神经网络（GNNs）在基于图结构的用户-物品交互数据上进行协同过滤（CF）任务中的表示学习方面展现出强大能力。然而，由于现有基于GNN的CF模型在相邻节点间固有的递归消息传播机制，结合低通拉普拉斯平滑算子，可能导致过平滑与噪声效应，从而生成难以区分且不准确的用户（物品）表示。此外，在整个图结构中通过堆叠聚合器进行递归信息传播，可能在实际应用中导致可扩展性较差。针对上述局限，我们提出一种简洁高效的协同过滤模型（SimRec），该模型融合了知识蒸馏与对比学习的优势。在SimRec中，教师GNN模型与轻量级学生网络之间实现自适应知识迁移，不仅保留了全局协同信号，还通过表示重校准解决了过平滑问题。在公开数据集上的实验结果表明，与多种强基线模型相比，SimRec在保持优异推荐性能的同时实现了更高效率。我们的实现已公开于：https://github.com/HKUDS/SimRec。