McGowan, Jamie;
Guest, Elizabeth;
Yan, Ziyang;
Zheng, Cong;
Patel, Neha;
Cusack, Mason;
Donaldson, Charlie;
... Dzogang, Fabon; + view all
(2023)
A Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail.
In: Pampin, HJC and Shirvany, R, (eds.)
Recommender Systems in Fashion and Retail. RECSYS 2022.
(pp. pp. 99-108).
Springer
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | A Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail |
| Event: | 4th Workshop on Recommender Systems in Fashion and Retail (FashionXrecsys) |
| Location: | Seattle, WA |
| Dates: | 18 Sep 2022 - 23 Sep 2022 |
| ISBN-13: | 978-3-031-22191-0 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-22192-7_6 |
| Publisher version: | https://doi.org/10.1007/978-3-031-22192-7_6 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Customer return prediction, Edge classification, Fashion retail dataset, Graph representation learning, Neural message passing |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10183628 |
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