Personalization using LSTMs
Personalization is a common theme in social networks and e-commerce businesses. However, personalization at Uber will involve understanding of how each driver/rider is expected to behave on the platform. One way to quantify future behavior is to understand the amount of trips a driver/rider will do. In this talk, I will present our work on training LSTMs for short term trip predictions (4-6 weeks) of each driver on the platform. Specifically, I would like to describe how we combine past engagement data of a particular driver with incentive budgets and use a custom loss function (i.e. zero inflated poisson) to come up with accurate trip predictions using LSTMs. Predicting rider/driver level behaviors can help us find cohorts of high performance drivers, run personalized offers to retain users, and deep dive into understanding of deviations from trip forecasts.
I am a data scientist at Uber focusing on solving forecasting problems using deep learning methods. Previously I worked in the finance industry using machine learning for researching quantitative trading strategies. I hold a Masters from UC Berkeley and a Masters from NYU.