scenarios/food_and_restaurants/README.md
Recommendation systems play a crucial role in the food service industry, from restaurant discovery platforms to food delivery apps. Studies show that personalized recommendations can increase order values by up to 20% in food delivery services.
Here are the key scenarios and considerations for food and restaurant recommendations.
This scenario helps users discover new restaurants based on their dining history, preferences, and context (location, time, occasion). Collaborative filtering approaches like ALS and NCF can be adapted for this purpose.
When a user is browsing a restaurant's menu, the system recommends dishes based on their past orders, dietary preferences, and popular combinations. This can include personalized recommendations and "frequently ordered together" suggestions.
For meal planning apps, the system suggests recipes based on dietary restrictions, nutritional goals, and ingredient availability. Content-based filtering approaches are particularly useful here.
This involves recommending different options based on time of day, day of week, or special occasions. For example, suggesting breakfast places in the morning or romantic restaurants for anniversary dinners.
Key data sources include user profiles (dietary preferences, allergies), order history, restaurant attributes (cuisine type, price range, location), menu items, and contextual data (time, weather, special occasions).
Common evaluation metrics include order conversion rate, average order value, and customer satisfaction ratings. A/B testing is crucial for measuring the impact of recommendations on business metrics.
Food and restaurant recommendations must account for various constraints including dietary restrictions, food allergies, delivery radius, restaurant capacity, and real-time availability. Seasonal menus and time-sensitive offerings also need to be considered in the recommendation strategy.