# Intelligent Online Selling Point Extraction for E-Commerce Recommendation

arXiv: https://arxiv.org/abs/2112.10613 · HTML: https://mlsanity.com/paper/2112.10613/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned](https://mlsanity.com/paper/1905.09418/) (2019)
- [Towards Knowledge-Based Personalized Product Description Generation in E-commerce](https://mlsanity.com/paper/1903.12457/) (2019)
- [Probing Product Description Generation via Posterior Distillation](https://mlsanity.com/paper/2103.01594/) (2021)
- [User-Inspired Posterior Network for Recommendation Reason Generation](https://mlsanity.com/paper/2102.07919/) (2021)
- [Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce](https://mlsanity.com/paper/1904.01735/) (2019)

## Built on by (most cited first, 6 of 6)

- [Retrieval-Augmented Code Generation for Universal Information Extraction](https://mlsanity.com/paper/2311.02962/) (2023)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [Automatic Controllable Product Copywriting for E-Commerce](https://mlsanity.com/paper/2206.10103/) (2022)
- [Automatic Generation of Product-Image Sequence in E-commerce](https://mlsanity.com/paper/2206.12994/) (2022)
- [Save, Revisit, Retain: A Scalable Framework for Enhancing User Retention in Large-Scale Recommender Systems](https://mlsanity.com/paper/2511.18013/) (2025)
- [Scenario-based Multi-product Advertising Copywriting Generation for E-Commerce](https://mlsanity.com/paper/2205.10530/) (2022)

## Abstract (arXiv)

In the past decade, automatic product description generation for e-commerce have witnessed significant advancement. 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. Since July 2020, IOSPE has become a core service for 62 key categories of products (covering more than 4 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 labour. 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.