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CineMatch is an advanced movie recommendation system developed as part of a project at Euromed University of Fez. It utilizes machine learning algorithms and data science techniques to provide personalized movie suggestions, enhancing user experience in digital entertainment.
- Algorithms Used: The system employs k-Nearest Neighbors (KNN) for collaborative filtering and neural networks (Multi-Layer Perceptron, MLP) for sophisticated recommendations.
- Filtering Techniques: Includes collaborative filtering (user-based and item-based), hybrid filtering, and content-based filtering focusing on movie genres.
- Data Source: Utilizes datasets like 'rating.csv' and 'movie.csv' from MovieLens for building and evaluating recommendation algorithms.
- Developed using Python and Flask for web application implementation.
- Includes an interface for both KNN and MLP-based recommendations.
- Utilizes TMDb API for fetching movie details and images.
Home interface :
Knn interface :
MLP interface :
