A Large-Scale Deep Architecture for Personalized Grocery Basket Recommendations
This addresses the business need for better recommendations in online grocery shopping, though it appears incremental as it builds on existing within-basket recommendation models.
The paper tackles the problem of providing personalized grocery basket recommendations in online shopping platforms by introducing RTT2Vec, a system that achieves a 9.4% uplift in prediction metrics over baseline models and an 11.6x speedup in inference.
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