Movies have always been a central part of our entertainment experience, but with thousands of titles available, how can we find the ones we’ll love most? That’s where recommendation systems come in. In this project, I explored building a movie recommendation system using the MovieLens dataset, leveraging both item-based and user-based collaborative filtering techniques.
About the Dataset
The MovieLens dataset is a widely used benchmark for recommendation systems. It contains over 25 million ratings from 162,000+ users on 59,000+ movies, along with movie titles and genres. Its size and diversity make it perfect for testing collaborative filtering algorithms.
Key points from the dataset:
- Most users rate only a few movies, and most movies receive few ratings.
- Some movies, like Forrest Gump and The Shawshank Redemption, are extremely popular, receiving thousands of ratings.
- Ratings tend to skew high, suggesting users generally rate movies they’ve enjoyed.
Source: MovieLens Dataset
Exploratory Data Analysis
Before building the recommendation system, I explored the data to understand its distribution and patterns. Some insights:
- The distribution of ratings shows that 4-star and 5-star ratings dominate, reflecting a positive bias in user ratings.
- Users’ activity is highly skewed—most users rate fewer than 20 movies, while a small group of highly active users rates hundreds.
- Similarly, a few movies dominate the rating counts, while the majority receive only a handful of ratings.
Visualizing these distributions helped in understanding which parts of the dataset would require careful handling during model building.
Building the Recommendation System
I implemented both item-based and user-based collaborative filtering using cosine similarity:
- Item-based: Predicts a user’s rating for a movie based on their ratings for similar movies.
- User-based: Predicts a rating based on ratings by similar users for the same movie.
To make computation manageable, I focused on a subset of the top 500 most active users and top 500 most rated movies, reducing the dataset to a size suitable for in-memory similarity calculations.
Evaluating Performance
To measure accuracy, I split the dataset into training and test sets and computed:
- RMSE (Root Mean Squared Error)
- MAE (Mean Absolute Error)
Results showed that both item-based and user-based models were effective, with slight differences depending on the dataset slice. These metrics provide a baseline for understanding the predictive quality of collaborative filtering.
Generating Recommendations
The system can generate top-N recommendations for any user, whether using item-based or user-based similarity. For example, for a given active user, the system can suggest movies they haven’t seen yet but are likely to enjoy, such as hidden gems or highly rated classics.
This makes the recommendation system not only a tool for prediction but also a personalized guide to discovering new movies.
Key Takeaways
- Collaborative filtering is powerful: Even without deep content analysis, similarity-based approaches can provide meaningful recommendations.
- Data exploration is crucial: Understanding distributions and biases helps in pre-processing and model design.
- Scalability matters: With tens of millions of ratings, sampling or smart filtering is necessary to make computation feasible.
- Personalization enhances experience: By focusing on individual user preferences, recommendation systems can uncover both popular and niche content.
This project was an exciting dive into practical machine learning and recommendation systems, combining data analysis, similarity computation, and prediction into a pipeline that can help users navigate the vast world of movies.
You can check out the full project and code on GitHub: Movie Recommendation System
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