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With growing consumer adoption of online grocery shopping through platformssuch as Amazon Fresh, Instacart, and Walmart Grocery, there is a pressingbusiness need to provide relevant recommendations throughout the customerjourney. In this paper, we introduce a production within-basket groceryrecommendation system, RTT2Vec, which generates real-time personalized productrecommendations to supplement the user's current grocery basket. We conductextensive offline evaluation of our system and demonstrate a 9.4% uplift inprediction metrics over baseline state-of-the-art within-basket recommendationmodels. We also propose an approximate inference technique 11.6x times fasterthan exact inference approaches. In production, our system has resulted in anincrease in average basket size, improved product discovery, and enabled fasteruser check-out

A Large-Scale Deep Architecture for Personalized Grocery Basket Recommendations