Recommendations and Search at Pinterest + Embeddings
Over 300 million users come to Pinterest monthly to discover ideas for their creative outlets through our recommendation and search products. Embeddings are a fundamental technology in these systems, powering the match engine to surface relevant and engaging content. We represent all aspects of the Pinterest ecosystem (pins, users, text, and images) under this common representation, enabling us to learn relationships across entity types to jointly optimize for product goals. Join us as we discuss how recent advancements in computer vision, natural language processing, and graph convolutional neural networks power embeddings and together enabled both new product experiences such as Pinterest Lens and improved performance of our core recommendation systems. We also discuss key infrastructure challenges and our solutions to scaling embedding search to web-scale systems.
Andrew is a Staff Software Engineer working in the Visual Search and Applied Science groups at Pinterest. During his career, he was the founding engineer and TL of visual search at Pinterest, leading multiple generations of serving, indexing, and modeling to build products including Pinterest Lens. More recently, he leads the embedding efforts at Pinterest to push the limits of recommendation systems. Andrew received his B.S at UC Berkeley and M.S at Stanford.