Real20M: A Large-scale E-commerce Dataset for Cross-domain Retrieval

Abstract: In e-commerce, products and micro-videos serve as two primary carriers. Introducing cross-domain retrieval between these carriers can establish associations, thereby leading to the advancement of specific scenarios, such as retrieving products based on micro-videos or recommending relevant videos based on products. However, existing datasets only focus on retrieval within the product domain while neglecting the micro-video domain and often ignore the multi-modal characteristics of the product domain. Additionally, these datasets strictly limit their data scale through content alignment and use a content-based data organization format that hinders the inclusion of user retrieval intentions. To address these limitations, we propose the PKU Real20M dataset, a large-scale e-commerce dataset designed for cross-domain retrieval. We adopt a query-driven approach to efficiently gather over 20 million e-commerce products and micro-videos, including multimodal information. Additionally, we design a three-level entity prompt learning framework to align inter-modality information from coarse to fine. Moreover, we introduce the Query-driven Cross-Domain retrieval framework (QCD), which leverages user queries to facilitate efficient alignment between the product and micro-video domains. Extensive experiments on two downstream tasks validate the effectiveness of our proposed approaches. The dataset and source code are available at https://github.com/PKU-ICST-MIPL/Real20M_ACMMM2023.

Recommended citation: Yanzhe Chen, Huasong Zhong, Xiangteng He, Yuxin Peng and Lele Cheng, "Real20M: A Large-scale E-commerce Dataset for Cross-domain Retrieval", ACM MM 2023.
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