Recently, sign-aware graph recommendation has drawn much attention as it will learn users' negative preferences besides positive ones from both positive and negative interactions (i.e., links in a graph) with items. To accommodate the different semantics of negative and positive links, existing works utilize two independent encoders to model users' positive and negative preferences, respectively. However, these approaches cannot learn the negative preferences from high-order heterogeneous interactions between users and items formed by multiple links with different signs, resulting in inaccurate and incomplete negative user preferences. To cope with these intractable issues, we propose a novel \textbf{L}ight \textbf{S}igned \textbf{G}raph Convolution Network specifically for \textbf{Rec}ommendation (\textbf{LSGRec}), which adopts a unified modeling approach to simultaneously model high-order users' positive and negative preferences on a signed user-item interaction graph. Specifically, for the negative preferences within high-order heterogeneous interactions, first-order negative preferences are captured by the negative links, while high-order negative preferences are propagated along positive edges. Then, recommendation results are generated based on positive preferences and optimized with negative ones. Finally, we train representations of users and items through different auxiliary tasks. Extensive experiments on three real-world datasets demonstrate that our method outperforms existing baselines regarding performance and computational efficiency. Our code is available at \url{https://anonymous.4open.science/r/LSGRec-BB95}.

翻译：最近，符号感知图推荐因其能从用户与物品的正向和负向交互（即图中的链接）中学习用户的正负偏好而受到广泛关注。为适应负向与正向链接的语义差异，现有方法采用两个独立编码器分别建模用户的正向和负向偏好。然而，这些方法无法从由多类不同符号链接构成的高阶异质用户-物品交互中学习负向偏好，导致用户负向偏好建模不准确且不完整。为解决这些棘手问题，我们提出了一种面向推荐的轻量符号图卷积网络（LSGRec），其采用统一建模方法在带符号的用户-物品交互图上同时建模用户的高阶正向和负向偏好。具体而言，针对高阶异质交互中的负向偏好：一阶负向偏好通过负向链接捕获，而高阶负向偏好则沿正向边传播。随后，基于正向偏好生成推荐结果，并通过负向偏好进行优化。最后，通过不同辅助任务训练用户与物品的表示。在三个真实数据集上的大量实验表明，本方法在性能和计算效率上均优于现有基线。代码见匿名链接：\url{https://anonymous.4open.science/r/LSGRec-BB95}。