为提升推荐精准度，提出直接优化Top-K指标的新架构。
TopKGAT: A Top-K Objective-Driven Architecture for Recommendation
- 基于Top-K指标的可微近似设计新模型结构。
- 在4个数据集上均超越现有最优方法，精度显著提升。
- 适合追求高精度推荐场景的开发者和研究者。
推荐系统旨在为用户召回最相关的前K项，常用精确率@K（Precision@K）和召回率@K（Recall@K）评估效果。模型架构作为归纳偏置，影响其学习模式。近年来涌现了多种推荐架构，涵盖传统矩阵分解、深度神经网络和图神经网络，但其设计常未显式对齐Top-K目标，制约性能。为此，本文提出TopKGAT，一种从可微近似Top-K指标推导出的新架构。单层TopKGAT的前向计算与Precision@K梯度上升动态天然一致，使模型能自然提升前K项推荐准确率。结构上，TopKGAT类似图注意力网络，实现高效。在四个基准数据集上的大量实验表明，TopKGAT持续优于现有先进基线。代码已开源：https://github.com/StupidThree/TopKGAT。
原文摘要 · Abstract (English)
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.
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