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.
@article{arxiv.1711.07601,
title = {Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time},
author = {Chantat Eksombatchai and Pranav Jindal and Jerry Zitao Liu and Yuchen Liu and Rahul Sharma and Charles Sugnet and Mark Ulrich and Jure Leskovec},
journal= {arXiv preprint arXiv:1711.07601},
year = {2017}
}