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.

翻译：相似商品推荐是电子商务中的关键任务，它帮助用户根据其感兴趣的商品探索同类及相关替代品。与传统机器学习模型不同，图神经网络（GNN）能够从设计层面理解商品之间的复杂关系（如相似性）。然而，尽管GNN在检索任务中广泛使用且侧重于优化相关性，现有GNN架构并未针对最大化收入相关目标（如总商品交易额GMV）进行设计，而GMV是电商企业的核心商业指标之一。此外，由于商品间关系的异质性，在大规模电商系统中为GNN定义准确的边关系并非易事。本文旨在通过设计一种名为GNN-GMVO（图神经网络-总商品交易额优化器）的新型GNN架构来解决上述问题。该模型在考虑商品间复杂关系的同时直接优化GMV。此外，我们提出了一种定制化边构建方法，使模型更适配相似商品推荐任务，并缓解噪声及复杂商品关系的影响。在三个真实数据集上的综合实验表明，与选定的前沿基准模型相比，我们模型推荐的排名靠前商品具有更高的预测性能与预期GMV。