The substitute-based recommendation is widely used in E-commerce to provide better alternatives to customers. However, existing research typically uses the customer behavior signals like co-view and view-but-purchase-another to capture the substitute relationship. Despite its intuitive soundness, we find that such an approach might ignore the functionality and characteristics of products. In this paper, we adapt substitute recommendation into language matching problem by taking product title description as model input to consider product functionality. We design a new transformation method to de-noise the signals derived from production data. In addition, we consider multilingual support from the engineering point of view. Our proposed end-to-end transformer-based model achieves both successes from offline and online experiments. The proposed model has been deployed in a large-scale E-commerce website for 11 marketplaces in 6 languages. Our proposed model is demonstrated to increase revenue by 19% based on an online A/B experiment.

翻译：替代推荐在电子商务中被广泛用于向客户提供更好的备选商品。然而，现有研究通常利用共浏览和“浏览后购买其他商品”等客户行为信号来捕捉替代关系。尽管这些方法在直觉上合理，但我们发现其可能忽略产品的功能与特性。本文将替代推荐转化为语言匹配问题，通过将商品标题描述作为模型输入来考虑产品功能。我们设计了一种新的数据变换方法，用于消除生产数据中的噪声信号。此外，本文从工程角度考虑了多语言支持。我们提出的端到端基于Transformer的模型在离线和在线实验中均取得了成功。该模型已在覆盖6种语言、11个市场的大型电商平台中部署。在线A/B实验表明，该模型能为营收带来19%的提升。