论文标题
基于变压器的替代建议模型，结合了弱监督的客户行为数据
A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data
论文作者
论文摘要
基于替代的建议在电子商务中广泛使用，为客户提供更好的替代方案。但是，现有的研究通常使用客户行为信号，例如共同观看和查看购买，但另一个可以捕获替代关系。尽管具有直观的健全性，我们发现这种方法可能会忽略产品的功能和特征。在本文中，我们通过将产品标题描述作为模型输入来考虑产品功能，将替代建议调整为语言匹配问题。我们设计了一种新的转换方法，以消除从生产数据中得出的信号。此外，我们从工程角度考虑了多语言支持。我们提出的基于端到端变压器的模型可以通过离线和在线实验取得成功。提出的模型已在一个大规模的电子商务网站上部署，该网站的11种市场，有6种语言。根据在线A/B实验，我们提出的模型被证明可以将收入增加19％。
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.
