Optimizing Recommendations for Food Delivery

When it comes to recommendations in the food delivery space, companies like Swiggy focus on multi-objective functions for ranking and relevance. This involves optimizing recommendations for the customer, the restaurant, and the driver, taking into account factors like customer preferences, restaurant stress levels, and driver capacity. The goal is to provide personalized and relevant recommendations in real-time, considering the time of day and location of the customer. Swiggy has developed algorithms and ML models to handle the complexity of optimizing recommendations at scale, using a variety of techniques including multi-objective functions and Lagrangian adoption to ensure the best possible customer experience.