A Large-Scale Deep Architecture for Personalized Grocery Basket Recommendations

Abstract

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 stateof-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.

An Artificial Intelligence of Things-Based Picking Algorithm for Online Shop in the Society 5.0’s Context

“…It is quite challenging to ensure that the massive implementation of artificial intelligence (AI) in online shopping takes effect. Currently, most of AI dominated systems are implemented in large-scale industries, such as Amazon, Walmart, BMW, and Mercedes Benz [ 1 , 2 , 3 ]. Separately, Industry 4.0 including techniques of AI, Internet of Things (IoT), robots, and online shopping has now shown tremendous benefits.…”