Intelligent online selling point extraction and generation for e-commerce recommendation

• Main Authors: Guo, Xiaojie, Wang, Shugen, Zhao, Hanqing, Diao, Shiliang, Chen, Jiajia, Ding, Zhuoye, He, Zhen, Lu, Jianchao, Xiao, Yun, Long, Bo, Yu, Han, Wu, Lingfei
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Computer and Information Science; Commerce platforms; Extraction systems
• Online Access: https://hdl.handle.net/10356/174292
• Institution: Nanyang Technological University

During the past decade, great significant advancements have been witnessed in the domain of automatic product description generation. As the services provided by e-commerce platforms become diverse, it is necessary to dynamically adapt the patterns of descriptions generated. The selling point of products is an important type of product description for which the length should be as short as possible while still conveying key information. In addition, this kind of product description should be eye-catching to the readers. Currently, product selling points are normally written by human experts. Thus, the creation and maintenance of these contents incur high costs. These costs can be significantly reduced if product selling points can be automatically generated by machines. In this paper, we report our experience developing and deploying the intelligent online selling point extraction (IOSPE) system to serve the recommendation system in the JD.com e-commerce platform. In addition, an interactive tool is released for merchants in JD.COM, which additionally allow them to select and customize the selling points with flexibility. Since July 2020, IOSPE has become a core service for 62 key categories of products (covering more than four million products). So far, it has generated more than 0.1 billion selling points, thereby significantly scaling up the selling point creation operation and saving human labor. These IOSPE-generated selling points have increased the click-through rate (CTR) by 1.89% and the average duration the customers spent on the products by more than 2.03% compared to the previous practice, which are significant improvements for such a large-scale e-commerce platform.