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.
Ankit currently works as a Senior Research Scientist at Uber AI Labs, the machine learning research arm of Uber. His work primarily involves the application of Deep Learning methods to a variety of Uber’s problems ranging from forecasting, food delivery to self driving cars. Previously, he has worked in variety of data science roles at Bank of America, Facebook and other startups. He has co-authored a book on machine learning titled “Tensorflow Machine Learning Projects”. Additionally, he has been a featured speaker in many of the top AI conferences and universities across US including UC Berkeley, OReilly AI conference etc. He completed his MS from UC Berkeley and BS from IIT Bombay (India).