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Welcome to today’s episode, where we dive into how Pinterest has revolutionized content retrieval with a large-scale learned retrieval system. With billions of pins and users, delivering relevant content efficiently is no small feat. Traditional search methods, reliant on keyword matching and manual feature engineering, often struggled to capture the complexity of user intent.

In response, Pinterest adopted an embedding-based retrieval system, leveraging deep learning to create high-dimensional vector representations of content and user queries. This shift has enabled faster, more accurate, and highly personalized content recommendations at scale.

In this episode, we’ll explore the challenges Pinterest faced, the architecture behind this system, and the impact it has had on user engagement. Stay tuned as we break down the future of large-scale retrieval systems and what this means for AI-driven recommendations!

Blog Post- https://medium.com/pinterest-engineering/establishing-a-large-scale-learned-retrieval-system-at-pinterest-eb0eaf7b92c5

Establishing a Large Scale Learned Retrieval System at Pinterest

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