A Large-Scale Deep Architecture for Personalized Grocery Basket Recommendations
Aditya Mantha, Yokila Arora, Shubham Gupta, Praveenkumar Kanumala, Zhiwei Liu, Stephen D. Guo, Kannan Achan
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.
BibTeX
@inproceedings{icassp2020_alargescaledeepa,
title = {A Large-Scale Deep Architecture for Personalized Grocery Basket Recommendations},
author = {Aditya Mantha and Yokila Arora and Shubham Gupta and Praveenkumar Kanumala and Zhiwei Liu and Stephen D. Guo and Kannan Achan},
booktitle = {ICASSP 2020},
year = {2020}
}