Movie Recommendation System
Overview
This project implements a movie recommendation system using linear algebra techniques, specifically matrix factorization. The system aims to provide personalized movie recommendations to users based on their past interactions with the platform.
Problem Statement
The primary challenge addressed by this project is to create a scalable recommendation system that can accurately predict user preferences, even in the presence of missing data. The system should be able to identify latent relationships between users and movies to offer relevant and engaging recommendations.
Approach
The recommendation system utilizes matrix factorization to analyze user-item interactions and uncover underlying patterns and preferences. By decomposing the user-item interaction matrix into lower-dimensional matrices, the system can predict user ratings for movies they have not yet seen. This approach enables the system to provide personalized recommendations tailored to each user's tastes and preferences.
Benefits
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Personalized Recommendations: Users receive movie recommendations based on their unique preferences and viewing history.
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Enhanced User Engagement: By offering relevant suggestions, the system increases user engagement and satisfaction.
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Scalability: The system is designed to handle large datasets and is scalable to accommodate growing user bases and movie libraries.
Dependencies
Python NumPy SciPy Pandas