Recommendation systems (RS) aim to retrieve the top-K items most relevant to users, with metrics such as Precision@K and Recall@K commonly used to assess effectiveness. The architecture of an RS model acts as an inductive bias, shaping the patterns the model is inclined to learn. In recent years, numerous recommendation architectures have emerged, spanning traditional matrix factorization, deep neural networks, and graph neural networks. However, their designs are often not explicitly aligned with the top-K objective, thereby limiting their effectiveness. To address this limitation, we propose TopKGAT, a novel recommendation architecture directly derived from a differentiable approximation of top-K metrics. The forward computation of a single TopKGAT layer is intrinsically aligned with the gradient ascent dynamics of the Precision@K metric, enabling the model to naturally improve top-K recommendation accuracy. Structurally, TopKGAT resembles a graph attention network and can be implemented efficiently. Extensive experiments on four benchmark datasets demonstrate that TopKGAT consistently outperforms state-of-the-art baselines. The code is available at https://github.com/StupidThree/TopKGAT.

翻译：推荐系统（RS）旨在检索与用户最相关的Top-K个项目，通常使用Precision@K和Recall@K等指标来评估其有效性。RS模型的架构作为一种归纳偏置，塑造了模型倾向于学习的数据模式。近年来，涌现了众多推荐架构，涵盖了传统的矩阵分解、深度神经网络和图神经网络。然而，这些架构的设计往往未能与Top-K目标明确对齐，从而限制了其推荐效能。为解决这一局限，我们提出了TopKGAT，一种直接从Top-K指标的可微分近似推导而来的新型推荐架构。单层TopKGAT的前向计算本质上与Precision@K指标的梯度上升动态保持一致，使得模型能够自然地提升Top-K推荐的准确性。在结构上，TopKGAT类似于图注意力网络，并且可以实现高效计算。在四个基准数据集上的大量实验表明，TopKGAT在性能上持续优于现有的先进基线模型。代码已公开于https://github.com/StupidThree/TopKGAT。