  Latent user representations are widely adopted in the tech industry for
powering personalized recommender systems. Most prior work infers a single high
dimensional embedding to represent a user, which is a good starting point but
falls short in delivering a full understanding of the user's interests. In this
work, we introduce PinnerSage, an end-to-end recommender system that represents
each user via multi-modal embeddings and leverages this rich representation of
users to provides high quality personalized recommendations. PinnerSage
achieves this by clustering users' actions into conceptually coherent clusters
with the help of a hierarchical clustering method (Ward) and summarizes the
clusters via representative pins (Medoids) for efficiency and interpretability.
PinnerSage is deployed in production at Pinterest and we outline the several
design decisions that makes it run seamlessly at a very large scale. We conduct
several offline and online A/B experiments to show that our method
significantly outperforms single embedding methods.
