Lyft hosts a dynamic marketplace connecting millions of people to a robust transportation network. In order to offer high value and quality service for both riders and drivers we need to make complex optimization decisions in near-real time. The environment can change quickly with traffic, events and weather, making these decisions even more challenging.
We have employed multi-arm bandits (MAB) algorithms, a common machine learning method for decision making using long-term rewards, to improve our real-time decision making capability. MABs allow us to not only iterate at a faster cadence and lower cost, but also allow for dynamic user experiences and responsive marketplace systems. We will walk through some of our most impactful MAB applications in UI optimization and personalized messaging, concluding with applications in our marketplace algorithms.