Modeling complementary relationships greatly helps recommender systems to accurately and promptly recommend the subsequent items when one item is purchased. Unlike traditional similar relationships, items with complementary relationships may be purchased successively (such as iPhone and Airpods Pro), and they not only share relevance but also exhibit dissimilarity. Since the two attributes are opposites, modeling complementary relationships is challenging. Previous attempts to exploit these relationships have either ignored or oversimplified the dissimilarity attribute, resulting in ineffective modeling and an inability to balance the two attributes. Since Graph Neural Networks (GNNs) can capture the relevance and dissimilarity between nodes in the spectral domain, we can leverage spectral-based GNNs to effectively understand and model complementary relationships. In this study, we present a novel approach called Spectral-based Complementary Graph Neural Networks (SComGNN) that utilizes the spectral properties of complementary item graphs. We make the first observation that complementary relationships consist of low-frequency and mid-frequency components, corresponding to the relevance and dissimilarity attributes, respectively. Based on this spectral observation, we design spectral graph convolutional networks with low-pass and mid-pass filters to capture the low-frequency and mid-frequency components. Additionally, we propose a two-stage attention mechanism to adaptively integrate and balance the two attributes. Experimental results on four e-commerce datasets demonstrate the effectiveness of our model, with SComGNN significantly outperforming existing baseline models.

翻译：建模互补关系能够极大地帮助推荐系统在用户购买某一商品后，准确且及时地推荐后续商品。不同于传统的相似关系，具有互补关系的商品可能被连续购买（例如iPhone与AirPods Pro），它们不仅具有相关性，同时也表现出相异性。由于这两种属性相互对立，对互补关系进行建模具有挑战性。先前尝试利用这些关系的研究要么忽视了相异性属性，要么对其进行了过度简化，导致建模效果不佳且无法平衡两种属性。由于图神经网络（GNNs）能够在谱域中捕捉节点间的相关性与相异性，我们可以利用基于谱方法的GNN来有效理解和建模互补关系。在本研究中，我们提出了一种名为基于谱方法的互补图神经网络（SComGNN）的新方法，该方法利用了互补商品图的谱特性。我们首次观察到互补关系由低频和中频分量构成，分别对应相关性属性和相异性属性。基于这一谱观测，我们设计了具有低通和中通滤波器的谱图卷积网络，以捕获低频和中频分量。此外，我们提出了一种两阶段注意力机制来自适应地整合并平衡这两种属性。在四个电子商务数据集上的实验结果表明了我们模型的有效性，SComGNN显著优于现有的基线模型。