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.

这篇论文介绍了一种名为 GNN-GMVO（Graph Neural Network - Gross Merchandise Value Optimizer）的新型 GNN 架构，旨在通过考虑物品之间复杂的关系并直接优化 GMV 来解决电子商务系统中的类似物品推荐问题。通过在三个实际数据集上进行全面实验，与一些最先进的基准模型相比，我们的模型在预测性能和预期 GMV 方面展现出更好的表现。

GNN-GMVO：面向相似商品推荐的图神经网络优化总商品价值

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

Sequential Recommendation (SR) models user dynamics and predicts the next
preferred items based on the user history. Existing SR methods model the 'was
interacted before' item-item transitions observed in sequences, which can be
viewed as an item relationship. However, there are multiple auxiliary item
relationships, e.g., items from similar brands and with similar contents in
real-world scenarios. Auxiliary item relationships describe item-item
affinities in multiple different semantics and alleviate the long-lasting cold
start problem in the recommendation. However, it remains a significant
challenge to model auxiliary item relationships in SR.
To simultaneously model high-order item-item transitions in sequences and
auxiliary item relationships, we propose a Multi-relational Transformer capable
of modeling auxiliary item relationships for SR (MT4SR). Specifically, we
propose a novel self-attention module, which incorporates arbitrary item
relationships and weights item relationships accordingly. Second, we regularize
intra-sequence item relationships with a novel regularization module to
supervise attentions computations. Third, for inter-sequence item relationship
pairs, we introduce a novel inter-sequence related items modeling module.
Finally, we conduct experiments on four benchmark datasets and demonstrate the
effectiveness of MT4SR over state-of-the-art methods and the improvements on
the cold start problem. The code is available at
this https URL.

对于顺序推荐中的辅助物品关系，提出了一个基于多关系变形器的顺序推荐方法进行建模，并通过实验表明其在冷启动问题等方面的有效性。