Accepted conference talk to Nvidia GTC 2021
Many ecommerce companies have extensive logs of user behavior such as clicks and conversions. However, if supervised learning is naively applied, then systems can suffer from poor performance due to bias and feedback loops. Using techniques from counterfactual learning we can leverage log data in a principled manner in order to model user behaviour and build personalized recommender systems. At Grubhub, a user journey begins with recommendations and the vast majority of conversions are powered by recommendations. Our recommender policies can drive user behavior to increase orders and/or profit. Accordingly, the ability to rapidly iterate and experiment is very important. Because of our powerful GPU workflows, we can iterate 200% more rapidly than with counterpart CPU workflows. Developers iterate ideas with notebooks powered by GPUs. Hyperparameter spaces are explored up to 8x faster with multi-GPUs Ray clusters. Solutions are shipped from notebooks to production in half the time with nbdev. With our accelerated DS workflows and Deep Learning on GPUs, we were able to deliver a +12.6% conversion boost in just a few months. In this talk we hope to present modern techniques for industrial recommender systems powered by GPU workflows. First a small background on counterfactual learning techniques, then followed by practical information and data from our industrial application.