Cross-Domain Product Representation Learning for Rich-Content E-Commerce

Xuehan Bai, Yan Li, Yanhua Cheng, Wenjie Yang, Quan Chen, Han Li; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 5697-5706

Abstract


The proliferation of short video and live-streaming platforms has revolutionized how consumers engage in online shopping. Instead of browsing product pages, consumers are now turning to rich-content e-commerce, where they can purchase products through dynamic and interactive media like short videos and live streams. This emerging form of online shopping has presented new opportunities for platforms to enhance user engagement and shopping experience. However, it has also introduced technical challenges, as products may be presented differently across various media domains. Therefore, a unified product representation is essential for achieving cross-domain product recognition to ensure an optimal user search experience and effective product recommendations. Despite the urgent industrial need for a unified cross-domain product representation, previous studies have predominantly focused only on product pages without taking into account short videos and live streams. To fill the gap in the rich-content e-commerce area, in this paper, we introduce a large-scale cross-domain poduct recognition dataset, called ROPE. ROPE covers a wide range of product categories and contains over 180,000 products, corresponding to millions of short videos and live streams. It is the first dataset to cover product pages, short videos, and live streams simultaneously, providing the basis for establishing a unified product representation across different media domains. Furthermore, we propose a cross-domain product representation framework, namely COPE, which unifies product representations in different domains through multimodal learning including text and vision. Extensive experiments on downstream tasks like cross-modal retrieval and classification demonstrate the effectiveness of COPE in learning a joint feature space for all product domains.

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[bibtex]
@InProceedings{Bai_2023_ICCV, author = {Bai, Xuehan and Li, Yan and Cheng, Yanhua and Yang, Wenjie and Chen, Quan and Li, Han}, title = {Cross-Domain Product Representation Learning for Rich-Content E-Commerce}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {5697-5706} }