Pinterest Harnesses AI for Smarter Content Recommendations

Pinterest has unveiled its latest approach to content recommendations, leveraging artificial intelligence to assess user behaviors and predict their intent. Rather than simply suggesting related pins, the platform now identifies complete “user journeys”—sequences of interactions revealing what users actually want to achieve, whether planning a wedding, renovating a kitchen, or learning a new skill.

How Pinterest Maps User Journeys

The AI system analyzes three primary data sources: user search history with timestamped queries, user activity history including pin interactions and clickthroughs, and content saved to user boards. Using clustering algorithms, the platform generates keyword clusters representing distinct “journey candidates.” Specialized models then rank journeys, predict stages, generate names, and expand recommendations based on this analysis. The system runs on a streaming infrastructure, enabling real-time updates as users’ behaviors evolve.

Results Speak for Themselves

The impact has been substantial. Pinterest has improved email click rates by 88% while user surveys show 23% more positive feedback through this updated recommendations approach. By understanding the broader context of each user’s goals and interests, Pinterest transforms from a simple discovery platform into an achievement-focused tool.

The platform employs large language models to generate personalized journey recommendations, which then drive email push notifications encouraging users to return and continue their journeys. This evolution demonstrates how AI-driven predictive models can significantly enhance user experience by anticipating what users want to see next based on their unique activity patterns and goals.