We take pride in creating exceptional products, elegant architecture and clean, simple code.
We operate at scale. We serve a lot of traffic with consistently fast page speeds. We process massive quantities of images and data every day.
We believe in building tools and systems, gathering stats and monitoring. We automate everything so that we can spend time on more interesting projects.
We give engineers a lot of latitude because we trust in their ability to make an impact.
MySQL, memcached, nginx, Solr, RabbitMQ, Hive, AWS (EC2, Route53, S3), Splunk.
Post 2: Data and the User ExperienceBy Matt Wheeler Last month we shared some of the technology behind Polyvore’s Style Profile and how we’re using machine learning to understand our users' individual style to recommend more personalized products and outfits. We discovered that our unique set data (our users create over 3 million sets every month) helps improve the recommendations to create a more engaging shopping and discovery experience for our users.
Over the past year, our engineering team has undertaken the task of creating a more personalized experience for our users. We already have an amazing community of designers, artists, and fashion enthusiasts who come to Polyvore to get inspired around shopping. However, we felt that with a little bit of machine learning we could help users discover and shop for even more products that they may not have found on their own.