Automatic Product Copywriting for E-Commerce
- URL: http://arxiv.org/abs/2112.11915v1
- Date: Wed, 15 Dec 2021 19:06:31 GMT
- Title: Automatic Product Copywriting for E-Commerce
- Authors: Xueying Zhang, Yanyan Zou, Hainan Zhang, Jing Zhou, Shiliang Diao,
Jiajia Chen, Zhuoye Ding, Zhen He, Xueqi He, Yun Xiao, Bo Long, Han Yu,
Lingfei Wu
- Abstract summary: The Automatic Product Copywriting Generation system has been deployed in JD.com since Feb 2021.
By Sep 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.
- Score: 46.1215290892261
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 pre-trained
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 Feb 2021. By Sep 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 Feb 2021.
Related papers
- Leveraging Large Language Models for Enhanced Product Descriptions in
eCommerce [6.318353155416729]
This paper introduces a novel methodology for automating product description generation using the LLAMA 2.0 7B language model.
We train the model on a dataset of authentic product descriptions from Walmart, one of the largest eCommerce platforms.
Our findings reveal that the system is not only scalable but also significantly reduces the human workload involved in creating product descriptions.
arXiv Detail & Related papers (2023-10-24T00:55:14Z) - Automatic Controllable Product Copywriting for E-Commerce [58.97059802658354]
We deploy an E-commerce Prefix-based Controllable Copywriting Generation into the JD.com e-commerce recommendation platform.
We conduct experiments to validate the effectiveness of the proposed EPCCG.
We introduce the deployed architecture which cooperates with the EPCCG into the real-time JD.com e-commerce recommendation platform.
arXiv Detail & Related papers (2022-06-21T04:18:52Z) - The YiTrans End-to-End Speech Translation System for IWSLT 2022 Offline
Shared Task [92.5087402621697]
This paper describes the submission of our end-to-end YiTrans speech translation system for the IWSLT 2022 offline task.
The YiTrans system is built on large-scale pre-trained encoder-decoder models.
Our final submissions rank first on English-German and English-Chinese end-to-end systems in terms of the automatic evaluation metric.
arXiv Detail & Related papers (2022-06-12T16:13:01Z) - Scenario-based Multi-product Advertising Copywriting Generation for
E-Commerce [46.29638014067242]
We propose an automatic Scenario-based Multi-product Advertising Copywriting Generation system (SMPACG) for E-Commerce.
The SMPACG has been developed for directly serving for our e-commerce recommendation system, and also used as a real-time writing assistant tool for merchants.
arXiv Detail & Related papers (2022-05-21T07:45:53Z) - Intelligent Online Selling Point Extraction for E-Commerce
Recommendation [41.983131116332636]
We develop and deploy the IOSPE system to serve the recommendation system in the JD.com e-commerce platform.
Since July 2020, IOSPE has generated more than 0.1 billion selling points.
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.
arXiv Detail & Related papers (2021-12-16T00:32:06Z) - The USYD-JD Speech Translation System for IWSLT 2021 [85.64797317290349]
This paper describes the University of Sydney& JD's joint submission of the IWSLT 2021 low resource speech translation task.
We trained our models with the officially provided ASR and MT datasets.
To achieve better translation performance, we explored the most recent effective strategies, including back translation, knowledge distillation, multi-feature reranking and transductive finetuning.
arXiv Detail & Related papers (2021-07-24T09:53:34Z) - Personalized Embedding-based e-Commerce Recommendations at eBay [3.1236273633321416]
We present an approach for generating personalized item recommendations in an e-commerce marketplace by learning to embed items and users in the same vector space.
Data ablation is incorporated into the offline model training process to improve the robustness of the production system.
arXiv Detail & Related papers (2021-02-11T17:58:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.