Powered by Machine Learning: Recommendation Systems at WeWork
Recommendation algorithms aim to improve the user experience and drive engagement by delivering personalized content, such as music, retail products, and social content. Team Rex at WeWork was formed to deploy machine learning applications to help our members create their life's work, and bridge the gap between their digital and physical experiences. One of our first products is a personalized newsfeed designed to surface the most relevant user-generated content for each member, such as posts, events and promotions. This application is powered by a suite of recommendation models embedded within a novel multi-armed bandit-based experimentation platform. This framework allows for continuous feedback loops and more granular optimizations compared to a standard A/B testing framework, as well as the ability to leverage a universe of adaptive NLP-based and collaborative filtering models.
Karry Lu is a senior data scientist at WeWork with interests in recommendation systems, NLP, and Bayesian inference. In previous lives, he has led machine learning at a (successfully exited!) foodtech startup, and fought crime for the feds with the power of econometrics. Even more previous lives include relapsed statistician, community organizer and failed novelist.