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Similar item recommendation is a critical task in the e-Commerce industry,which helps customers explore similar and relevant alternatives based on theirinterested products. Despite the traditional machine learning models, GraphNeural Networks (GNNs), by design, can understand complex relations likesimilarity between products. However, in contrast to their wide usage inretrieval tasks and their focus on optimizing the relevance, the current GNNarchitectures are not tailored toward maximizing revenue-related objectivessuch as Gross Merchandise Value (GMV), which is one of the major businessmetrics for e-Commerce companies. In addition, defining accurate edge relationsin GNNs is non-trivial in large-scale e-Commerce systems, due to theheterogeneity nature of the item-item relationships. This work aims to addressthese issues by designing a new GNN architecture called GNN-GMVO (Graph NeuralNetwork - Gross Merchandise Value Optimizer). This model directly optimizes GMVwhile considering the complex relations between items. In addition, we proposea customized edge construction method to tailor the model toward similar itemrecommendation task and alleviate the noisy and complex item-item relations. Inour comprehensive experiments on three real-world datasets, we show higherprediction performance and expected GMV for top ranked items recommended by ourmodel when compared with selected state-of-the-art benchmark models.

GNN-GMVO: Graph Neural Networks for Optimizing Gross Merchandise Value in Similar Item Recommendation