Speaker "Ankit Jain" Details Back



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 Data Scientist at Uber where his primary focus is on forecasting and self driving car's business problems.Prior to that, he has worked in a variety of data science roles at Runnr, Facebook, BofA and Clearslide. Ankit holds a Masters from UC Berkeley and BS from IIT Bombay (India).