Movie Recommendation System
Project Overview:
This project focuses on building a movie recommender system that uses machine learning algorithms to suggest movies tailored to individual user preferences. By analyzing user ratings and movie features, the system can recommend movies that are likely to match a user's tastes.
Objective:
The goal is to create a reliable and efficient recommendation engine that can help users discover movies they’ll enjoy, based on their past ratings and preferences.
Technologies Used:
Collaborative Filtering and Content-Based Filtering methods for recommendations.
Python with libraries such as
pandas,scikit-learn, andsurprisefor machine learning.MovieLens Dataset for training and testing the model.
Features
Collaborative Filtering
Leverages the preferences of similar users to recommend movies a user hasn’t watched yet.
Content-based Filtering
Suggests movies based on a user’s previous interactions, considering the features of the movies (e.g., genre, cast, director).
Hybrid Approach
Combines collaborative and content-based filtering for more accurate and diverse recommendations.
Collaborative Filtering:
I implemented a collaborative filtering model using matrix factorization techniques, such as Singular Value Decomposition (SVD), to predict missing ratings based on user-item interactions.
Content-Based Filtering:
This model suggests movies based on their features (genre, actors, etc.) and a user's past liked movies. Cosine similarity was used to measure how closely related movies are based on their attributes.
Hybrid Approach:
To balance the strengths of both methods, I combined collaborative and content-based filtering to provide more diverse and accurate recommendations. This hybrid approach ensures that the system can recommend niche or popular movies depending on the user's profile.
Results & Evaluation
Evaluation Metrics:
The performance of the recommender system was measured using metrics such as RMSE (Root Mean Square Error) and Precision@K. These metrics assess how accurately the model predicts user ratings and how relevant the recommendations are.
Model Performance:
The collaborative filtering model achieved a solid RMSE score, indicating that the predicted ratings were close to the actual ratings. Precision@K demonstrated that the recommendations were both accurate and relevant to the users' preferences.
Sample Output:
Below is an example of recommendations provided to a user who has shown a preference for action movies:
User Input: Rated "Die Hard", "The Dark Knight", and "Mad Max: Fury Road" highly.
Recommendations: "John Wick", "The Avengers", "Inception".
Challenges & Future Work
Challenges:
Cold Start Problem: A common challenge in recommender systems, especially for new users with little to no history.
Scalability: Dealing with large datasets can lead to performance bottlenecks, particularly in real-time recommendation settings.
Future Enhancements:
Enhanced Cold Start Solutions: Implement additional strategies like demographic-based filtering or external data (e.g., social media preferences) to address cold start issues.
Model Tuning: Further optimization of hyperparameters for both content-based and collaborative filtering models.
Real-Time Recommendations: Build a pipeline that updates the recommendations in real time as new data comes in.
Dataset:
This project uses the MovieLens dataset, which contains millions of user ratings for various movies. The dataset includes details such as movie titles, genres, user ratings, and timestamps.
Data Preprocessing:
Missing data handling and cleaning processes were applied to ensure high-quality inputs.
The dataset was transformed into user-item matrices, which are essential for collaborative filtering.
Features like movie genres were vectorized for use in content-based filtering.