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arXiv · 2403.11593

End-to-end multi-modal product matching in fashion e-commerce

Sándor Tóth·Stephen Wilson·Alexia Tsoukara·Enric Moreu·Anton Masalovich·Lars Roemheld
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Abstract

Product matching, the task of identifying different representations of the same product for better discoverability, curation, and pricing, is a key capability for online marketplace and e-commerce companies. We present a robust multi-modal product matching system in an industry setting, where large datasets, data distribution shifts and unseen domains pose challenges. We compare different approaches and conclude that a relatively straightforward projection of pretrained image and text encoders, trained through contrastive learning, yields state-of-the-art results, while balancing cost and performance. Our solution outperforms single modality matching systems and large pretrained models, such as CLIP. Furthermore we show how a human-in-the-loop process can be combined with model-based predictions to achieve near perfect precision in a production system.

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Sándor Tóth, Stephen Wilson, Alexia Tsoukara, Enric Moreu, Anton Masalovich, Lars Roemheld. 2024-03-18. End-to-end multi-modal product matching in fashion e-commerce. https://arxiv.org/abs/2403.11593

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