Summary

The article discusses how Pinterest has developed a system to identify user journeys, which are sequences of user-item interactions that reveal user interests, intents, and contexts. By leveraging user data and machine learning techniques, Pinterest aims to enhance its recommendation system, moving beyond immediate interests to long-term user goals. The approach includes dynamic keyword extraction, clustering, and journey ranking, which collectively improve user engagement through personalized notifications. The article outlines the system architecture, key components, and the impact of journey-aware notifications on user interactions.

Data Problems Addressed