A Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail

• URL: http://arxiv.org/abs/2302.14096v1
• Date: Mon, 27 Feb 2023 19:14:37 GMT
• Title: A Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail
• Authors: Jamie McGowan, Elizabeth Guest, Ziyang Yan, Cong Zheng, Neha Patel, Mason Cusack, Charlie Donaldson, Sofie de Cnudde, Gabriel Facini and Fabon Dzogang
• Abstract summary: We present a novel dataset collected by ASOS to address the challenge of predicting customer returns in a fashion retail ecosystem. We first explore the structure of this dataset with a focus on the application of Graph Representation Learning. We show examples of a return prediction classification task with a selection of baseline models and a graph representation based model.
• Score: 0.243788455857269
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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