  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.
