Automatic product copywriting for e-commerce

• Main Authors: Zou, Yanyan, Zhang, Xueying, Zhou, Jing, Diao, Shiliang, Chen, Jiajia, Ding, Zhuoye, He, Zhen, He, Xueqi, Xiao, Yun, Long, Bo, Ma, Mian, Xu, Sulong, Yu, Han, Wu, Lingfei
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Computer and Information Science; Product recommendation; E-commerce
• Online Access: https://hdl.handle.net/10356/174284
• Institution: Nanyang Technological University

Product copywriting is a critical component of e-commerce recommendation platforms. It aims to attract users' interest and improve user experience by highlighting product characteristics with textual descriptions. In this paper, we report our experience deploying the proposed Automatic Product Copywriting Generation (APCG) system into the JD.com e-commerce product recommendation platform. It consists of two main components: (1) natural language generation, which is built from a transformer–pointer network and a pretrained sequence-to-sequence model based on millions of training data from our in-house platform; and (2) copywriting quality control, which is based on both automatic evaluation and human screening. For selected domains, the models are trained and updated daily with the updated training data. In addition, the model is also used as a real-time writing assistant tool on our live broadcast platform. The APCG system has been deployed in JD.com since February 2021. By September 2021, it has generated 2.53 million product descriptions, and improved the overall averaged click-through rate (CTR) and the conversion rate (CVR) by 4.22 and 3.61%, compared to baselines, respectively, on a year-on-year basis. The accumulated gross merchandise volume (GMV) made by our system is improved by 213.42%, compared to the number in February 2021.