Related Pins is the Web-scale recommender system that powers over 40% of user engagement on Pinterest.
Over the past three years Pinterest has experimented with several visual search and recommendation services, including Related Pins (2014), Similar Looks (2015), Flashlight (2016) and Lens (2017).
In this paper, we focus on training and evaluating effective word embeddings with both text and visual information.
This paper presents Pinterest Related Pins, an item-to-item recommendation system that combines collaborative filtering with content-based ranking.
We demonstrate that, with the availability of distributed computation platforms such as Amazon Web Services and open-source tools, it is possible for a small engineering team to build, launch and maintain a cost-effective, large-scale visual search system with widely available tools.