  At Pinterest, we utilize image embeddings throughout our search and
recommendation systems to help our users navigate through visual content by
powering experiences like browsing of related content and searching for exact
products for shopping. In this work we describe a multi-task deep metric
learning system to learn a single unified image embedding which can be used to
power our multiple visual search products. The solution we present not only
allows us to train for multiple application objectives in a single deep neural
network architecture, but takes advantage of correlated information in the
combination of all training data from each application to generate a unified
embedding that outperforms all specialized embeddings previously deployed for
each product. We discuss the challenges of handling images from different
domains such as camera photos, high quality web images, and clean product
catalog images. We also detail how to jointly train for multiple product
objectives and how to leverage both engagement data and human labeled data. In
addition, our trained embeddings can also be binarized for efficient storage
and retrieval without compromising precision and recall. Through comprehensive
evaluations on offline metrics, user studies, and online A/B experiments, we
demonstrate that our proposed unified embedding improves both relevance and
engagement of our visual search products for both browsing and searching
purposes when compared to existing specialized embeddings. Finally, the
deployment of the unified embedding at Pinterest has drastically reduced the
operation and engineering cost of maintaining multiple embeddings while
improving quality.
