Recipe Recommendation System

Description:

The Recipe Recommendation System project aims to develop a web application that provides personalized recipe recommendations to users based on their culinary preferences, dietary restrictions, and cooking history. By leveraging machine learning algorithms and collaborative filtering techniques, the application offers tailored recipe suggestions to inspire users and enhance their cooking experience.

Features:

User Profiling: Analyzes user cooking habits, ingredient preferences, and dietary restrictions to create personalized user profiles.

Recommendation Engine: Utilizes machine learning algorithms such as collaborative filtering, content-based filtering, and natural language processing to generate accurate recipe recommendations.

Customization Options: Allows users to specify cuisine preferences, ingredient allergies, dietary preferences (e.g., vegetarian, gluten-free), and cooking skill level to tailor the recipe suggestions accordingly.

Recipe Rating and Reviews: Enables users to rate recipes, leave reviews, and provide feedback, fostering a community-driven platform for sharing culinary experiences.

Ingredient Substitution: Offers suggestions for ingredient substitutions based on user preferences or dietary requirements, ensuring flexibility and adaptability in recipe recommendations.

Real-Time Updates: Provides real-time recommendations based on user interactions and dynamically adjusts suggestions as user preferences evolve.

Cross-Platform Compatibility: Supports integration with various cooking apps and platforms, ensuring accessibility across desktop and mobile devices.