  With growing consumer adoption of online grocery shopping through platforms
such as Amazon Fresh, Instacart, and Walmart Grocery, there is a pressing
business need to provide relevant recommendations throughout the customer
journey. In this paper, we introduce a production within-basket grocery
recommendation system, RTT2Vec, which generates real-time personalized product
recommendations to supplement the user's current grocery basket. We conduct
extensive offline evaluation of our system and demonstrate a 9.4% uplift in
prediction metrics over baseline state-of-the-art within-basket recommendation
models. We also propose an approximate inference technique 11.6x times faster
than exact inference approaches. In production, our system has resulted in an
increase in average basket size, improved product discovery, and enabled faster
user check-out
