  User experience in modern content discovery applications critically depends
on high-quality personalized recommendations. However, building systems that
provide such recommendations presents a major challenge due to a massive pool
of items, a large number of users, and requirements for recommendations to be
responsive to user actions and generated on demand in real-time. Here we
present Pixie, a scalable graph-based real-time recommender system that we
developed and deployed at Pinterest. Given a set of user-specific pins as a
query, Pixie selects in real-time from billions of possible pins those that are
most related to the query. To generate recommendations, we develop Pixie Random
Walk algorithm that utilizes the Pinterest object graph of 3 billion nodes and
17 billion edges. Experiments show that recommendations provided by Pixie lead
up to 50% higher user engagement when compared to the previous Hadoop-based
production system. Furthermore, we develop a graph pruning strategy at that
leads to an additional 58% improvement in recommendations. Last, we discuss
system aspects of Pixie, where a single server executes 1,200 recommendation
requests per second with 60 millisecond latency. Today, systems backed by Pixie
contribute to more than 80% of all user engagement on Pinterest.
