We present a novel dataset collected by ASOS (a major online fashion retailer) to address the challenge of predicting customer returns in a fashion retail ecosystem. With the release of this substantial dataset we hope to motivate further collaboration between research communities and the fashion industry. We first explore the structure of this dataset with a focus on the application of Graph Representation Learning in order to exploit the natural data structure and provide statistical insights into particular features within the data. In addition to this, we show examples of a return prediction classification task with a selection of baseline models (i.e. with no intermediate representation learning step) and a graph representation based model. We show that in a downstream return prediction classification task, an F1-score of 0.792 can be found using a Graph Neural Network (GNN), improving upon other models discussed in this work. Alongside this increased F1-score, we also present a lower cross-entropy loss by recasting the data into a graph structure, indicating more robust predictions from a GNN based solution. These results provide evidence that GNNs could provide more impactful and usable classifications than other baseline models on the presented dataset and with this motivation, we hope to encourage further research into graph-based approaches using the ASOS GraphReturns dataset.

翻译：我们提出由ASOS（一家大型在线时尚零售商）收集的新型数据集，旨在应对时尚零售生态系统中客户退货预测的挑战。通过发布这一大规模数据集，我们期待推动研究界与时尚产业间的深度合作。首先，我们探索该数据集的结构特性，重点关注图表示学习的应用，以利用其天然的数据结构特征，并提供数据中特定特征的统计洞察。此外，我们展示了退货预测分类任务的示例，包括若干基线模型（即无中间表示学习步骤）与基于图表示的模型。研究表明，在下游退货预测分类任务中，使用图神经网络（GNN）可获得0.792的F1分数，优于本文讨论的其他模型。伴随F1分数的提升，通过将数据重构为图结构，我们还实现了更低的交叉熵损失，表明基于GNN的方案能产生更稳健的预测。这些结果证明，在该数据集上，GNN能比其他基线模型提供更具影响力和实用性的分类。基于此动机，我们期待鼓励更多利用ASOS GraphReturns数据集开展图方法相关的研究。