Creating an AI system to recommend movies based on a user's favorite movies and books involves several steps. Here's a high-level overview of how you can build such a system:

1. Data Collection

• Movies: Collect data on movies, including genres, directors, cast, plot summaries, and user reviews.
• Books: Collect data on books, including genres, authors, summaries, and user reviews.
• User Profiles: Collect data on user preferences, including their favorite movies and books.

2. Data Preprocessing

• Clean and preprocess the data to ensure consistency.
• Create feature vectors for movies and books based on their attributes.

3. Similarity Calculation

• Use techniques like TF-IDF, word embeddings, or other NLP methods to represent the plot summaries and descriptions.
• Calculate similarity scores between movies and books using cosine similarity or other distance metrics.

4. Recommendation Algorithm

• Implement a recommendation algorithm that finds movies similar to the user's favorite movies and books.

5. User Interface

• Create a simple interface where users can input their favorite movies and books and receive recommendations.

Implementation Steps

Step 1: Data Collection

You can use APIs such as TMDB (The Movie Database) for movies and Google Books API for books to collect data.

Step 2: Data Preprocessing

Use libraries like pandas, scikit-learn, and NLTK for preprocessing.