How to Create Personalized Recommendations with Surprise in Python

Learn how to build powerful recommendation systems with Python's Surprise library. Create personalized predictions quickly using SVD and collaborative filtering.

Making recommendations should feel simple and accurate – and that’s exactly what Surprise helps with, surprise, it’s a new ML library. It’s made just for building recommender systems, helping you figure out what users might like based on what they’ve done before.

This makes it easier to build models that suggest things – whether it’s movies, books, products, or whatever else you can think of. You can try out different algorithms, see how well they work, and adjust them to fit what you need without getting stuck in complicated code.

What’s also really cool is that this library is built on top of the popular scikit-learn library for Machine Learning. As we get new the launch of our new Machine Learning series I felt this is a great starter. The series will launch soon!

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Surprise gives you what you need to quickly build recommendation models, whether you’re using built-in datasets or your own custom ones. If you’re trying to recommend the next show to binge, guess how someone will rate something, or suggest products they might like, Surprise makes the whole thing feel more like science and less like guessing.

I’ve run into a few bumps while trying to fine-tune the models, but that’s part of the process. Today, I’ll walk you through getting Surprise set up, training your first model, and testing how well it works – and it won’t take much code to do it.

The best thing about Surprise is how easy it is to switch between algorithms and see how they compare. With just a little effort, you can use more advanced methods like SVD (Singular Value Decomposition) or KNN (K-Nearest Neighbors), which can make your recommendation engine stronger and ready for growth.

But it’s not just for small projects. Surprise can handle bigger datasets, run cross-validation, and help fine-tune models, which makes it a great pick for more complex systems too.

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I’m looking forward to digging deeper into this and creating more advanced projects with Surprise down the line that will pair with our new upcoming series. The code I’m showing today is just a starting point to help you get things up and running.

Let’s get started by installing Surprise:

pip3 install scikit-surprise

Now kick back and let the algorithms handle the hard part!

This Week’s Surprise Tips