Surprise is an easy-to-use open source Python library for recommender systems. Its goal is to make life easier for reseachers who want to play around with new algorithms ideas, for teachers who want some teaching materials, and for students.

Surprise was designed with the following purposes in mind:

- Give the user perfect control over his experiments. To this end, a strong emphasis is laid on documentation, which we have tried to make as clear and precise as possible by pointing out every details of the algorithms. - Alleviate the pain of Dataset handling. Users can use both built-in datasets (Movielens, Jester), and their own custom datasets. - Provide with various ready-to-use prediction algorithms (Neighborhood approaches, SVD, SVD++...) - Make it easy to implement new algorithm ideas. - Provide with tools to evaluate, analyse and compare the algorithms performance. Cross-validation procedures can be run very easily.

Code Quality Rank: L4
Programming language: Python
License: GNU General Public License v3.0 or later
Latest version: v1.1.1

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Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data.

Surprise was designed with the following purposes in mind:

The name SurPRISE (roughly :) ) stands for Simple Python RecommendatIon System Engine.

Please note that surprise does not support implicit ratings or content-based information.

Getting started, example

Here is a simple example showing how you can (down)load a dataset, split it for 5-fold cross-validation, and compute the MAE and RMSE of the SVD algorithm.

from surprise import SVD
from surprise import Dataset
from surprise.model_selection import cross_validate

# Load the movielens-100k dataset (download it if needed).
data = Dataset.load_builtin('ml-100k')

# Use the famous SVD algorithm.
algo = SVD()

# Run 5-fold cross-validation and print results.
cross_validate(algo, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)


Evaluating RMSE, MAE of algorithm SVD on 5 split(s).

            Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std
RMSE        0.9311  0.9370  0.9320  0.9317  0.9391  0.9342  0.0032
MAE         0.7350  0.7375  0.7341  0.7342  0.7375  0.7357  0.0015
Fit time    6.53    7.11    7.23    7.15    3.99    6.40    1.23
Test time   0.26    0.26    0.25    0.15    0.13    0.21    0.06

Surprise can do much more (e.g, GridSearchCV)! You'll find more usage examples in the documentation .


Here are the average RMSE, MAE and total execution time of various algorithms (with their default parameters) on a 5-fold cross-validation procedure. The datasets are the Movielens 100k and 1M datasets. The folds are the same for all the algorithms. All experiments are run on a notebook with Intel Core i5 7th gen (2.5 GHz) and 8Go RAM. The code for generating these tables can be found in the benchmark example.

Movielens 100k RMSE MAE Time
SVD 0.934 0.737 0:00:11
SVD++ 0.92 0.722 0:09:03
NMF 0.963 0.758 0:00:15
Slope One 0.946 0.743 0:00:08
k-NN 0.98 0.774 0:00:10
Centered k-NN 0.951 0.749 0:00:10
k-NN Baseline 0.931 0.733 0:00:12
Co-Clustering 0.963 0.753 0:00:03
Baseline 0.944 0.748 0:00:01
Random 1.514 1.215 0:00:01
Movielens 1M RMSE MAE Time
SVD 0.873 0.686 0:02:13
SVD++ 0.862 0.673 2:54:19
NMF 0.916 0.724 0:02:31
Slope One 0.907 0.715 0:02:31
k-NN 0.923 0.727 0:05:27
Centered k-NN 0.929 0.738 0:05:43
k-NN Baseline 0.895 0.706 0:05:55
Co-Clustering 0.915 0.717 0:00:31
Baseline 0.909 0.719 0:00:19
Random 1.504 1.206 0:00:19


With pip (you'll need numpy, and a C compiler. Windows users might prefer using conda):

$ pip install numpy
$ pip install scikit-surprise

With conda:

$ conda install -c conda-forge scikit-surprise

For the latest version, you can also clone the repo and build the source (you'll first need Cython and numpy):

$ pip install numpy cython
$ git clone https://github.com/NicolasHug/surprise.git
$ cd surprise
$ python setup.py install

License and reference

This project is licensed under the BSD 3-Clause license, so it can be used for pretty much everything, including commercial applications. Please let us know how Surprise is useful to you!

Please make sure to cite the paper if you use Surprise for your research:

  doi = {10.21105/joss.02174},
  url = {https://doi.org/10.21105/joss.02174},
  year = {2020},
  publisher = {The Open Journal},
  volume = {5},
  number = {52},
  pages = {2174},
  author = {Nicolas Hug},
  title = {Surprise: A Python library for recommender systems},
  journal = {Journal of Open Source Software}


The following persons have contributed to Surprise:

ashtou, bobbyinfj, caoyi, Олег Демиденко, Charles-Emmanuel Dias, dmamylin, Lauriane Ducasse, Marc Feger, franckjay, Lukas Galke, Tim Gates, Pierre-François Gimenez, Zachary Glassman, Jeff Hale, Nicolas Hug, Janniks, jyesawtellrickson, Doruk Kilitcioglu, Ravi Raju Krishna, Hengji Liu, Maher Malaeb, Manoj K, James McNeilis, Naturale0, nju-luke, Jay Qi, Lucas Rebscher, Skywhat, David Stevens, TrWestdoor, Victor Wang, Mike Lee Williams, Jay Wong, Chenchen Xu, YaoZh1918.

Thanks a lot :) !

Development Status

Starting from version 1.1.0 (September 19), we will only maintain the package and provide bugfixes. No new features will be considered.

For bugs, issues or questions about Surprise, please use the GitHub project page. Please don't send emails (we will not answer).

*Note that all licence references and agreements mentioned in the Surprise README section above are relevant to that project's source code only.