  Product ranking is a crucial component for many e-commerce services. One of
the major challenges in product search is the vocabulary mismatch between query
and products, which may be a larger vocabulary gap problem compared to other
information retrieval domains. While there is a growing collection of neural
learning to match methods aimed specifically at overcoming this issue, they do
not leverage the recent advances of large language models for product search.
On the other hand, product ranking often deals with multiple types of
engagement signals such as clicks, add-to-cart, and purchases, while most of
the existing works are focused on optimizing one single metric such as
click-through rate, which may suffer from data sparsity. In this work, we
propose a novel end-to-end multi-task learning framework for product ranking
with BERT to address the above challenges. The proposed model utilizes
domain-specific BERT with fine-tuning to bridge the vocabulary gap and employs
multi-task learning to optimize multiple objectives simultaneously, which
yields a general end-to-end learning framework for product search. We conduct a
set of comprehensive experiments on a real-world e-commerce dataset and
demonstrate significant improvement of the proposed approach over the
state-of-the-art baseline methods.
