Sarah Haq

Understanding collector profiles to recommend art

The art market is an extremely complex industry, with even more complex collectors. Our recommendations need to be good enough to convince someone to make a massive financial investment, but how do we use machine learning algorithms to re-create the emotional connection a collector has when adding a piece to their collection? In this talk, I will discuss how I went about building recommendation models at Artsy to help collectors discover and buy art they truly love.

Sarah is a Senior Machine Learning Engineer at the world's largest online art marketplace, Artsy, and a Lecturer at the Karlsruhe University of Applied Sciences and Leuphana University. She has over ten years of experience working with data and building machine learning models for various startups, from underwear companies to unicorns. She is currently shaping the personalisation strategy at Artsy and building a recommendation engine to connect collectors with artworks they will love. The art market is an extremely complex industry, with even more complex collectors. The recommendations need to be good enough to convince someone to make a massive financial investment, but how do we use machine learning algorithms to re-create the emotional connection a collector has when adding a piece to their collection? Sarah will be presenting at our Enterprise AI Summit in Berlin on 4-5 October to address this question.

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