This Jupyter Notebook provides a basic Python script for building a recommendation system with collaborative filtering using the Surprise library. The script uses the MovieLens dataset, which is built-in in the Surprise library.
Collaborative filtering is a popular technique used in recommendation systems. In this example, we will use the Surprise library to build a recommendation system based on collaborative filtering. The script loads the MovieLens dataset, defines the algorithm object, performs cross-validation to evaluate the model’s performance, and makes predictions on a test set.
The following libraries are required for this analysis:
You can install these libraries using pip:
pip install scikit-surprise
The code for this analysis can be found in the following GitHub repository: Link to GitHub Repository
In this example, we demonstrated how to build a recommendation system with collaborative filtering using the Surprise library. We imported the necessary libraries, loaded the MovieLens dataset, defined the algorithm object, performed cross-validation to evaluate the model’s performance, and made predictions on a test set.
Please note that this is a basic example of a recommendation system and there are many ways to improve it. For example, you can try using more advanced algorithms like SVD or NMF, tune the parameters of the algorithm, or incorporate additional information such as user or item features.
After training your model, you can use it to predict the rating a user would give to an item they have not interacted with. The script provides an example of how to do this by splitting the dataset into a training set and a testing set, training the algorithm on the training set, and then predicting ratings for the test set. The accuracy of the predictions is evaluated using the root mean square error (RMSE).
Additionally, the script includes a loop to print the user and item IDs, the actual rating, and the predicted rating for the first 10 instances in the test set. This can be useful for understanding how well the recommendation system is performing.
Please note that this is a simple evaluation method, and there are more complex evaluation metrics available, such as precision@k or recall@k, which consider the ranking of recommendations rather than just the predicted ratings. The choice of evaluation method depends on the specific requirements of your recommendation system.
By following this example, you can apply collaborative filtering techniques to build your own recommendation system and make personalized recommendations to users based on their interactions with items. You can further enhance this analysis by exploring different algorithms, tuning their parameters, incorporating additional data sources, or implementing hybrid recommendation systems that combine collaborative filtering with other techniques like content-based filtering.
Remember that building a recommendation system is an iterative process that involves data exploration, model selection, evaluation, and continuous improvement. It’s important to experiment with different approaches and evaluate their performance to find the best solution for your specific use case.
By following this example and leveraging the capabilities of the Surprise library, you can create powerful recommendation systems that provide personalized suggestions to users, enhancing their experience and driving engagement on your platform.
Please note that when deploying a recommendation system in a production environment, it’s crucial to consider scalability, privacy, and ethical considerations. Ensure that you handle large datasets efficiently, protect user data, and comply with relevant regulations and guidelines.
Feel free to explore the code provided and adapt it to your specific needs. The possibilities are endless when it comes to building recommendation systems, and the Surprise library provides a solid foundation for implementing collaborative filtering algorithms. Happy recommending!