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 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.
Abstract (translated)
质量产品描述对于在电子商务平台中提供具有竞争力的客户体验至关重要。准确而有吸引力的描述不仅有助于客户做出明智的决定，而且还能提高购买的可能性。然而，制作一个成功的产品描述是冗长和非常耗时的。由于其重要性，自动生成产品描述吸引了研究界和工业界的相当大的兴趣。现有的方法主要使用模板或统计方法，它们的性能可能相当有限。
URL
https://arxiv.org/abs/1903.12457
