A hybrid recommendation system that combines collaborative filtering and content-based filtering approaches to suggest personalized vacation itineraries for users. The system also incorporates reinforcement learning with human feedback to continually improve recommendations over time.

These instructions will help you set up and run the recommendation system on your local machine for development and testing purposes.

You will need the following Python packages installed:

numpy pandas scipy scikit-learn gensim You can install these packages using pip:

Copy code pip install numpy pandas scipy scikit-learn gensim

Prepare your data files in CSV format as follows:

1. customers.csv: Contains user features such as age, gender, location, and travel preferences.
2. itineraries.csv: Contains item features such as destination, duration, travel_theme, points_of_interest, included_meals, accommodation, transportation, price, and activities.
3. interaction_data.csv: Contains user-item interaction data, such as user_id, item_id, and interaction value (e.g., ratings or number of bookings).

1. Load and preprocess the data (cleaning, handling missing values, etc.).
2. Extract and preprocess user and item features.
3. Create an interaction matrix.
4. Normalize the user_features and item_features.
5. Use the hybrid recommendation function to generate recommendations for individual users.
6. Incorporate reinforcement learning with human feedback to improve recommendations over time. Refer to the code snippets provided in this conversation for the steps involved in data preparation, feature extraction, and recommendation generation.