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：建模互补关系能极大帮助推荐系统在用户购买一件商品后，准确且及时地推荐后续商品。与传统的相似关系不同，具有互补关系的商品可能被依次购买（如iPhone与Airpods Pro），它们既共享相关性又表现出差异性。由于这两种属性相互对立，建模互补关系颇具挑战性。以往尝试利用这些关系的研究要么忽略、要么过度简化了差异性属性，导致建模效果不佳，且无法平衡两种属性。由于图神经网络（GNN）可在谱域捕捉节点间的相关性与差异性，我们可以利用基于谱的GNN有效理解并建模互补关系。本研究提出了一种名为基于谱的互补图神经网络（SComGNN）的新方法，利用互补商品图谱的谱特性。我们首次观察到互补关系由低频分量和中频分量组成，分别对应相关性属性和差异性属性。基于这一谱观察，我们设计了具有低通滤波器和中通滤波器的谱图卷积网络，用于捕捉低频和中频分量。此外，我们还提出了一种两阶段注意力机制，以自适应地整合并平衡这两种属性。在四个电商数据集上的实验结果表明，我们的模型具有有效性，SComGNN显著优于现有基线模型。