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：我们提出了一个由ASOS（一家大型在线时尚零售商）收集的新型数据集，旨在解决时尚零售生态系统中预测客户退货的挑战。通过发布这一大规模数据集，我们希望促进研究界与时尚产业之间的进一步合作。我们首先探索了该数据集的结构，重点关注图表示学习的应用，以利用其自然数据结构，并提供对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，包括一组基线模型（即无中间表示学习步骤）和基于图表示的模型。我们表明，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。除这一提升的F1分数外，我们还将数据重构为图结构，从而降低了交叉熵损失，这表明基于GNN的解决方案能生成更稳健的预测。这些结果证明，GNN在给定数据集上能提供比基线模型更具影响力且更可用的分类结果。基于这一动机，我们希望鼓励使用ASOS GraphReturns数据集进一步研究基于图的方法。