In recommender systems, most graph-based methods focus on positive user feedback, while overlooking the valuable negative feedback. Integrating both positive and negative feedback to form a signed graph can lead to a more comprehensive understanding of user preferences. However, the existing efforts to incorporate both types of feedback are sparse and face two main limitations: 1) They process positive and negative feedback separately, which fails to holistically leverage the collaborative information within the signed graph; 2) They rely on MLPs or GNNs for information extraction from negative feedback, which may not be effective. To overcome these limitations, we introduce SIGformer, a new method that employs the transformer architecture to sign-aware graph-based recommendation. SIGformer incorporates two innovative positional encodings that capture the spectral properties and path patterns of the signed graph, enabling the full exploitation of the entire graph. Our extensive experiments across five real-world datasets demonstrate the superiority of SIGformer over state-of-the-art methods. The code is available at https://github.com/StupidThree/SIGformer.

翻译：在推荐系统中，大多数基于图的方法仅关注用户的正反馈，而忽略了具有价值的负反馈。将正负反馈整合形成符号图，能够更全面地理解用户偏好。然而，现有整合两类反馈的研究较为稀疏且存在两大局限性：1）分别处理正负反馈，未能整体利用符号图中的协同信息；2）通过MLP或GNN从负反馈中提取信息可能效果不佳。为克服这些局限，我们提出SIGformer——一种基于Transformer架构的符号感知图推荐新方法。SIGformer引入两种创新的位置编码，分别捕捉符号图的谱特性与路径模式，从而实现对全图的充分利用。在五个真实数据集上的大量实验表明，SIGformer在性能上优于当前最优方法。代码已开源：https://github.com/StupidThree/SIGformer。