FoodNet - Simplifying Online Food Ordering with Contextual Food Combos.

Bundling complementary dishes into easy-to-order food combos is vital to providing a seamless food ordering experience. Manually curating combos across several thousands of restaurants and millions of dishes is neither scalable nor can be personalized. We propose FoodNet, an attention-based deep learning architecture with a monotonically decreasing constraint of diversity, to recommend personalized two-item combos from across different restaurants. In a large-scale evaluation involving  200 million candidate combos, we show that FoodNet outperforms the Transformer based model by 1.3%, the Siamese network based model by 13.6%, and the traditional Apriori baseline by 18.8% in terms of NDCG, which are significant improvements at our scale. We also present qualitative results to show the importance of attention and lattice layers in the proposed architecture.