Context-Aware Drive-thru Recommendation Service at Fast Food Restaurants
This addresses the need for effective recommendation systems in drive-thru services, offering a solution that can generalize to other customer interaction channels, though it is incremental in adapting existing methods to a specific domain.
The paper tackles the problem of generating food recommendations in drive-thru fast food scenarios where user attributes are unavailable, proposing the Transformer Cross Transformer (TxT) model that uses order behavior and contextual features, achieving superior results in Burger King's production environment.
Drive-thru is a popular sales channel in the fast food industry where consumers can make food purchases without leaving their cars. Drive-thru recommendation systems allow restaurants to display food recommendations on the digital menu board as guests are making their orders. Popular recommendation models in eCommerce scenarios rely on user attributes (such as user profiles or purchase history) to generate recommendations, while such information is hard to obtain in the drive-thru use case. Thus, in this paper, we propose a new recommendation model Transformer Cross Transformer (TxT), which exploits the guest order behavior and contextual features (such as location, time, and weather) using Transformer encoders for drive-thru recommendations. Empirical results show that our TxT model achieves superior results in Burger King's drive-thru production environment compared with existing recommendation solutions. In addition, we implement a unified system to run end-to-end big data analytics and deep learning workloads on the same cluster. We find that in practice, maintaining a single big data cluster for the entire pipeline is more efficient and cost-saving. Our recommendation system is not only beneficial for drive-thru scenarios, and it can also be generalized to other customer interaction channels.