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 pEronalized (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.

在电商平台中，我们通过结合神经网络和知识库提出了一种个性化的商品描述生成模型（KOBE），该模型通过考虑多种重要因素进行文本生成；实验结果证明，KOBE比现有方法（包括模板和统计方法）表现更好，可实现BLEU指标提高9.7%。该框架已在中国最大的在线电商平台淘宝上部署。

面向基于知识的个性化电商产品描述生成