# Towards Knowledge-Based Personalized Product Description Generation in E-commerce

arXiv: https://arxiv.org/abs/1903.12457 · HTML: https://mlsanity.com/paper/1903.12457/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2019.

## Built on by (most cited first, 30 of 32)

- [When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities](https://mlsanity.com/paper/2307.16376/) (2023)
- [A Survey on Multi-modal Summarization](https://mlsanity.com/paper/2109.05199/) (2021)
- [Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling](https://mlsanity.com/paper/2112.13556/) (2021)
- [Probing Product Description Generation via Posterior Distillation](https://mlsanity.com/paper/2103.01594/) (2021)
- [Controllable and Diverse Text Generation in E-commerce](https://mlsanity.com/paper/2102.11497/) (2021)
- [Recall and Learn: A Memory-augmented Solver for Math Word Problems](https://mlsanity.com/paper/2109.13112/) (2021)
- [Product Question Answering in E-Commerce: A Survey](https://mlsanity.com/paper/2302.08092/) (2023)
- [Automatic Product Copywriting for E-Commerce](https://mlsanity.com/paper/2112.11915/) (2021)
- [EcomGPT-CT: Continual Pre-training of E-commerce Large Language Models with Semi-structured Data](https://mlsanity.com/paper/2312.15696/) (2023)
- [LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following](https://mlsanity.com/paper/2308.04913/) (2023)
- [Intelligent Online Selling Point Extraction for E-Commerce Recommendation](https://mlsanity.com/paper/2112.10613/) (2021)
- [Aspect-based Analysis of Advertising Appeals for Search Engine Advertising](https://mlsanity.com/paper/2204.11445/) (2022)
- [Automatic Controllable Product Copywriting for E-Commerce](https://mlsanity.com/paper/2206.10103/) (2022)
- [INSPIRED2: An Improved Dataset for Sociable Conversational Recommendation](https://mlsanity.com/paper/2208.04104/) (2022)
- [A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation](https://mlsanity.com/paper/2402.13587/) (2024)
- [Attract me to Buy: Advertisement Copywriting Generation with Multimodal Multi-structured Information](https://mlsanity.com/paper/2205.03534/) (2022)
- [Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels](https://mlsanity.com/paper/2307.01969/) (2023)
- [Generating Attractive and Authentic Copywriting from Customer Reviews](https://mlsanity.com/paper/2404.13906/) (2024)
- [Stick to Facts: Towards Fidelity-oriented Product Description Generation](https://mlsanity.com/paper/2503.08454/) (2025)
- [Personalized Image Descriptions from Attention Sequences](https://mlsanity.com/paper/2512.06662/) (2025)
- [CUSTOM: Aspect-Oriented Product Summarization for E-Commerce](https://mlsanity.com/paper/2108.08010/) (2021)
- [Scenario-based Multi-product Advertising Copywriting Generation for E-Commerce](https://mlsanity.com/paper/2205.10530/) (2022)
- [GGP: A Graph-based Grouping Planner for Explicit Control of Long Text Generation](https://mlsanity.com/paper/2108.07998/) (2021)
- [Generating Rich Product Descriptions for Conversational E-commerce Systems](https://mlsanity.com/paper/2111.15298/) (2021)
- [Draft, Command, and Edit: Controllable Text Editing in E-Commerce](https://mlsanity.com/paper/2208.05623/) (2022)
- [INFACT: An Online Human Evaluation Framework for Conversational Recommendation](https://mlsanity.com/paper/2209.03213/) (2022)
- [IFDID: Information Filter upon Diversity-Improved Decoding for Diversity-Faithfulness Tradeoff in NLG](https://mlsanity.com/paper/2210.13829/) (2022)
- [Towards Zero-Shot Personalized Table-to-Text Generation with Contrastive Persona Distillation](https://mlsanity.com/paper/2304.08911/) (2023)
- [MMAPS: End-to-End Multi-Grained Multi-Modal Attribute-Aware Product Summarization](https://mlsanity.com/paper/2308.11351/) (2023)
- [Modeling Comparative Logical Relation with Contrastive Learning for Text Generation](https://mlsanity.com/paper/2406.09095/) (2024)

## Abstract (arXiv)

Quality product descriptions are critical for providing competitive customer experience in an e-commerce platform. An accurate and attractive description not only helps customers make an informed decision but also improves the likelihood of purchase. However, crafting a successful product description is tedious and highly time-consuming. Due to its importance, automating the product description generation has attracted considerable interests from both research and industrial communities. Existing methods mainly use templates or statistical methods, and their performance could be rather limited. In this paper, we explore a new way to generate the personalized product description by combining the power of neural networks and knowledge base. Specifically, we propose a KnOwledge Based pErsonalized (or KOBE) product description generation model in the context of e-commerce. In KOBE, we extend the encoder-decoder framework, the Transformer, to a sequence modeling formulation using self-attention. In order to make the description both informative and personalized, KOBE considers a variety of important factors during text generation, including product aspects, user categories, and knowledge base, etc. Experiments on real-world datasets demonstrate that the proposed method out-performs the baseline on various metrics. KOBE can achieve an improvement of 9.7% over state-of-the-arts in terms of BLEU. We also present several case studies as the anecdotal evidence to further prove the effectiveness of the proposed approach. The framework has been deployed in Taobao, the largest online e-commerce platform in China.