Presented By O'Reilly and Cloudera
Make Data Work
March 13–14, 2017: Training
March 14–16, 2017: Tutorials & Conference
San Jose, CA

Building a sustainable content ecosystem at Pinterest

Grace Huang (Pinterest)
1:50pm2:30pm Thursday, March 16, 2017
Secondary topics:  Media, Text
Average rating: ***..
(3.33, 3 ratings)

What you'll learn

  • Explore the lifecycle of a piece of content in Pinterest
  • Discover a portfolio of metrics Pinterest developed to monitor the health of the content corpus
  • Learn the story of creating a cross-functional initiative to preserve a healthy, sustainable content ecosystem


As Pinterest undergoes explosive growth in its user base, the content corpus that users contribute to also expands rapidly. The content and the machine-learning algorithms exist in a feedback cycle where content signals power the algorithms and algorithms influence the content that populates the ecosystem. A major algorithm change can significantly impact how content is exposed. Without proactive monitoring in place, the makeup of the exposed content can drift over time, in a direction that may not be the most beneficial to users.

Grace Huang offers a deep dive into the lifecycle of a piece of Pinterest content, from its birth through its growth and finally to its death, and explores how this process has changed over time. Along the way, Grace explains how a panel of metrics and a content-specific experiment framework were developed to help Pinterest gauge its content ecosystem health in different markets, helping answer questions such as how fast can we activate a piece of content absent of historical signals, do we have enough content to support a new market, and how has the makeup of the content corpus shifted over time? Grace concludes by sharing the story of how a cross-functional effort was bootstrapped to ensure that quality content can thrive in the the ecosystem and make its way to pinners who will find it relevant and engaging.

Photo of Grace Huang

Grace Huang


Grace Huang is the data science lead for discovery at Pinterest, where discovery products like recommendations and personalization are developed. She is passionate about building data science products around machine-learning algorithms to drive better experience for Pinterest users and build a sustainable ecosystem.