A transformer-based substitute recommendation model incorporating weakly supervised customer behavior data

The substitute-based recommendation is widely used in E-commerce to provide better alternatives to customers. However, existing re-search typically uses customer behavior signals like co-view and view-but-purchase-another to capture the substitute relationship. Despite its intuitive soundness, such an approach might ignore the functionality and characteristics of products. In this paper, we adapt substitute recommendations into language matching problem. It takes the 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.