  Similar item recommendation is a critical task in the e-Commerce industry,
which helps customers explore similar and relevant alternatives based on their
interested products. Despite the traditional machine learning models, Graph
Neural Networks (GNNs), by design, can understand complex relations like
similarity between products. However, in contrast to their wide usage in
retrieval tasks and their focus on optimizing the relevance, the current GNN
architectures are not tailored toward maximizing revenue-related objectives
such as Gross Merchandise Value (GMV), which is one of the major business
metrics for e-Commerce companies. In addition, defining accurate edge relations
in GNNs is non-trivial in large-scale e-Commerce systems, due to the
heterogeneity nature of the item-item relationships. This work aims to address
these issues by designing a new GNN architecture called GNN-GMVO (Graph Neural
Network - Gross Merchandise Value Optimizer). This model directly optimizes GMV
while considering the complex relations between items. In addition, we propose
a customized edge construction method to tailor the model toward similar item
recommendation task and alleviate the noisy and complex item-item relations. In
our comprehensive experiments on three real-world datasets, we show higher
prediction performance and expected GMV for top ranked items recommended by our
model when compared with selected state-of-the-art benchmark models.
