Intelligent online selling point extraction and generation for e-commerce recommendation
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 pro...
| Main Authors: | , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/174292 |
| _version_ | 1824456846252965888 |
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| author | Guo, Xiaojie Wang, Shugen Zhao, Hanqing Diao, Shiliang Chen, Jiajia Ding, Zhuoye He, Zhen Lu, Jianchao Xiao, Yun Long, Bo Yu, Han Wu, Lingfei |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Guo, Xiaojie Wang, Shugen Zhao, Hanqing Diao, Shiliang Chen, Jiajia Ding, Zhuoye He, Zhen Lu, Jianchao Xiao, Yun Long, Bo Yu, Han Wu, Lingfei |
| author_sort | Guo, Xiaojie |
| collection | NTU |
| description | 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. |
| first_indexed | 2025-02-19T04:00:35Z |
| format | Journal Article |
| id | ntu-10356/174292 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T04:00:35Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1742922024-03-29T15:36:23Z Intelligent online selling point extraction and generation for e-commerce recommendation Guo, Xiaojie Wang, Shugen Zhao, Hanqing Diao, Shiliang Chen, Jiajia Ding, Zhuoye He, Zhen Lu, Jianchao Xiao, Yun Long, Bo Yu, Han Wu, Lingfei School of Computer Science and Engineering Computer and Information Science Commerce platforms Extraction systems 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. Agency for Science, Technology and Research (A*STAR) Nanyang Technological University National Research Foundation (NRF) Published version Han Yu is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG2‐RP‐2020‐019); the Nanyang Assistant Professorship (NAP); and the RIE 2020 Advanced Manufacturing and Engineering (AME) Programmatic Fund (No. A20G8b0102), Singapore. 2024-03-25T08:12:09Z 2024-03-25T08:12:09Z 2023 Journal Article Guo, X., Wang, S., Zhao, H., Diao, S., Chen, J., Ding, Z., He, Z., Lu, J., Xiao, Y., Long, B., Yu, H. & Wu, L. (2023). Intelligent online selling point extraction and generation for e-commerce recommendation. AI Magazine, 44(1), 16-29. https://dx.doi.org/10.1002/aaai.12083 0738-4602 https://hdl.handle.net/10356/174292 10.1002/aaai.12083 2-s2.0-85167452044 1 44 16 29 en AISG2-RP-2020-019 A20G8b0102 AI Magazine © 2023 The Authors. AI Magazine published by Wiley Periodicals LLC on behalf of the Association for the Advancement of Artificial Intelligence. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivsLicense, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. application/pdf |
| spellingShingle | Computer and Information Science Commerce platforms Extraction systems Guo, Xiaojie Wang, Shugen Zhao, Hanqing Diao, Shiliang Chen, Jiajia Ding, Zhuoye He, Zhen Lu, Jianchao Xiao, Yun Long, Bo Yu, Han Wu, Lingfei Intelligent online selling point extraction and generation for e-commerce recommendation |
| title | Intelligent online selling point extraction and generation for e-commerce recommendation |
| title_full | Intelligent online selling point extraction and generation for e-commerce recommendation |
| title_fullStr | Intelligent online selling point extraction and generation for e-commerce recommendation |
| title_full_unstemmed | Intelligent online selling point extraction and generation for e-commerce recommendation |
| title_short | Intelligent online selling point extraction and generation for e-commerce recommendation |
| title_sort | intelligent online selling point extraction and generation for e commerce recommendation |
| topic | Computer and Information Science Commerce platforms Extraction systems |
| url | https://hdl.handle.net/10356/174292 |
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