Introducing a sophisticated hybrid movie recommendation system 🎬

This system, built using Python, combines the power of content-based and collaborative filtering techniques to offer personalized movie recommendations to users.

Github Link: https://github.com/Felicia197/Movie-Recommendation-System

💡 Key Features:

> Hybrid Approach: Leveraging both content-based filtering (cosine similarity) and collaborative filtering (SVD), our system provides accurate and diverse movie recommendations.

> Content-Based Filtering: Utilizing techniques such as CountVectorizer and linear kernel cosine similarity, we identify the 25 most similar movies based on a given movie title.

> Collaborative Filtering: Employing Singular Value Decomposition (SVD) from the Surprise library, we predict the ratings that a user might give to these 25 movies.

> Top Recommendations: Finally, we present the top 10 movies with the highest predicted ratings, ensuring that users receive personalized and relevant suggestions.

🚀 How It Works:

> Input: The system takes in a movie title and user as input.

> Content-Based Filtering: Using our content-based model, we compute the 25 most similar movies to the input title.

> Recommendations: Finally, we return the top 10 movies with the highest predicted ratings, tailored to the user's preferences.