  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.
