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：建模互补关系有助于推荐系统在用户购买某件商品后准确且及时地推荐后续商品。与传统相似关系不同，具有互补关系的商品可能被相继购买（如iPhone和Airpods Pro），它们既共享相关性又存在差异性。由于这两种属性相互对立，建模互补关系具有挑战性。以往利用这些关系的方法要么忽略差异属性，要么过度简化该属性，导致建模效率低下且无法平衡两种属性。鉴于图神经网络（GNNs）能在谱域中捕获节点间的相关性和差异性，我们可以借助基于谱域的GNNs有效理解并建模互补关系。本文提出一种新颖方法——基于谱的互补图神经网络（SComGNN），利用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成分构成，分别对应相关性和差异性属性。基于这一谱观察，我们设计了配备低通和中通滤波器的谱图卷积网络来捕获低频和中频成分。此外，我们提出一种两阶段注意力机制，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模型的有效性，SComGNN显著优于现有基线模型。