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Irene Iriarte - Building The Perfect Personalised Menu Using Python
Irene Iriarte shares expert techniques for building perfect personalized menus using Python, covering unique ingredients, curation, genetic algorithms, and data-driven approaches to create tailored experiences and optimize operations.
- Minimize unique ingredients: Reduce the number of unique ingredients to improve efficiency and lower costs.
- Curation: Introduce a curation process to balance variety and excitement in the menu.
- Genetic Algorithm: Use a genetic algorithm to create personalized menus, which reduces the time spent on menu planning and improves customer satisfaction.
- Multi-objective optimization: Use multi-objective optimization to consider multiple factors, such as variety, choice, and cost, when creating menus.
- Content-based recommendations: Implement content-based recommendations to suggest recipes based on customer preferences and ordering behavior.
- Collaborative filtering: Use collaborative filtering to suggest recipes based on customer similarity.
- Personalized collection: Create a personalized collection of recipes for each customer, based on their preferences and ordering history.
- Hybrid ordering: Combine content-based and collaborative filtering recommendations to create a hybrid ordering system.
- Menu optimization: Optimize menus to ensure they meet operational constraints, such as budget and ingredient availability.
- Data-driven approach: Take a data-driven approach to menu creation, using algorithms and machine learning to create personalized menus.
- Choice: Offer a wide range of recipes to customers, with options for different dietary requirements and preferences.
- Variety: Prioritize variety in the menu to ensure customers have a new and exciting experience with each order.
- Operational constraints: Consider operational constraints, such as budget and ingredient availability, when creating menus.
- Customer feedback: Use customer feedback to improve menu creation and personalization over time.
- Future development: Plan to continue improving menu creation and personalization using machine learning and user feedback.