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

## TL;DR

This paper introduces KOBE, a neural network model that combines knowledge bases and personalization factors to generate more informative and tailored product descriptions in e-commerce, outperforming existing methods.

## Contribution

The paper proposes a novel knowledge-based personalized description generation model extending Transformer with personalization and knowledge integration.

## Key findings

- KOBE outperforms baselines with 9.7% BLEU improvement.
- The model effectively incorporates product aspects, user categories, and knowledge.
- Deployed successfully in Taobao, demonstrating real-world applicability.

## Abstract

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.