Cross-Domain Product Representation Learning for Rich-Content E-Commerce
Xuehan Bai Yan Li Yanhua Cheng Wenjie Yang Quan Chen Han Li
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 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 Product Ecognition
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
demonstrate the effectiveness of COPE in learning a joint feature space for all
product domains.