This project implements a Movie Recommendation System that suggests movies to users based on their preferences using various recommendation algorithms such as Collaborative Filtering, Content-Based Filtering, and Hybrid Recommender Systems. It leverages machine learning techniques and a movie dataset to provide personalized movie recommendations.
The TMDB 5000 Movie Dataset from kaggle https://www.kaggle.com/datasets/tmdb/tmdb-movie-metadata
This Movie Recommendation System allows users to receive personalized movie suggestions based on their viewing history or similar user preferences. The system can use Collaborative Filtering, which recommends movies based on the ratings and preferences of similar users, and Content-Based Filtering, which recommends movies based on the features (e.g., genre, director) of the movies that a user has liked.
- Personalized movie recommendations based on user input.
- Collaborative filtering for user-item interactions.
- Content-based recommendations based on movie metadata.
- Hybrid recommender system combining both approaches for more accurate results.
- Python (for data processing, machine learning models)
- pandas (data manipulation)
- scikit-learn (machine learning algorithms)
- numpy (numerical operations)
- matplotlib & seaborn (visualization)
- Streamlit (for web application interface)
- Cosine Similarity: Developed a movie recommendation system using cosine similarity, a method for measuring the similarity between two vectors of an inner product space. Cosine similarity is used here to recommend movies based on user preferences and movie features.
- Natural Language Processing (NLP): Applied NLP techniques for text processing to convert movie metadata such as movie descriptions into numerical features.
- TF-IDF (Term Frequency-Inverse Document Frequency): Implemented vectorization techniques such as TF-IDF to convert text data into numerical features, improving the recommendation accuracy by capturing the importance of terms in movie descriptions.