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https://github.com/NicolasHug/Surprise

A Python scikit for building and analyzing recommender systems
https://github.com/NicolasHug/Surprise

factorization machine-learning matrix recommendation recommender svd systems

Last synced: 8 days ago
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A Python scikit for building and analyzing recommender systems

Host: GitHub
URL: https://github.com/NicolasHug/Surprise
Owner: NicolasHug
License: bsd-3-clause
Created: 2016-10-23T14:59:38.000Z (about 8 years ago)
Default Branch: master
Last Pushed: 2024-06-16T11:25:37.000Z (5 months ago)
Last Synced: 2024-10-14T12:04:38.678Z (20 days ago)
Topics: factorization, machine-learning, matrix, recommendation, recommender, svd, systems
Language: Python
Homepage: http://surpriselib.com
Size: 6.65 MB
Stars: 6,373
Watchers: 145
Forks: 1,012
Open Issues: 87
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md

Awesome Lists containing this project

awesome - Surprise - A Python scikit for building and analyzing recommender systems (Python)
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awesome-list - Surprise - A Python scikit for building and analyzing recommender systems. (Recommendation, Advertisement & Ranking / Others)
starred-awesome - Surprise - A Python scikit for building and analyzing recommender systems (Python)
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StarryDivineSky - NicolasHug/Surprise
awesome - NicolasHug/Surprise - A Python scikit for building and analyzing recommender systems (Python)

README

        [![GitHub version](https://badge.fury.io/gh/nicolashug%2FSurprise.svg)](https://badge.fury.io/gh/nicolashug%2FSurprise)

[![Documentation Status](https://readthedocs.org/projects/surprise/badge/?version=stable)](https://surprise.readthedocs.io/en/stable/?badge=stable)

[![python versions](https://img.shields.io/badge/python-3.8+-blue.svg)](https://surpriselib.com)

[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)

[![DOI](https://joss.theoj.org/papers/10.21105/joss.02174/status.svg)](https://doi.org/10.21105/joss.02174)

[![logo](./logo_black.svg)](https://surpriselib.com)

Overview

--------

[Surprise](https://surpriselib.com) is a Python

[scikit](https://projects.scipy.org/scikits.html) for building and analyzing

recommender systems that deal with explicit rating data.

[Surprise](https://surpriselib.com) **was designed with the

following purposes in mind**:

- Give users perfect control over their experiments. To this end, a strong

  emphasis is laid on

  [documentation](https://surprise.readthedocs.io/en/stable/index.html), which we

  have tried to make as clear and precise as possible by pointing out every

  detail of the algorithms.

- Alleviate the pain of [Dataset

  handling](https://surprise.readthedocs.io/en/stable/getting_started.html#load-a-custom-dataset).

  Users can use both *built-in* datasets

  ([Movielens](https://grouplens.org/datasets/movielens/),

  [Jester](https://eigentaste.berkeley.edu/dataset/)), and their own *custom*

  datasets.

- Provide various ready-to-use [prediction

  algorithms](https://surprise.readthedocs.io/en/stable/prediction_algorithms_package.html)

  such as [baseline

  algorithms](https://surprise.readthedocs.io/en/stable/basic_algorithms.html),

  [neighborhood

  methods](https://surprise.readthedocs.io/en/stable/knn_inspired.html), matrix

  factorization-based (

  [SVD](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVD),

  [PMF](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#unbiased-note),

  [SVD++](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVDpp),

  [NMF](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.NMF)),

  and [many

  others](https://surprise.readthedocs.io/en/stable/prediction_algorithms_package.html).

  Also, various [similarity

  measures](https://surprise.readthedocs.io/en/stable/similarities.html)

  (cosine, MSD, pearson...) are built-in.

- Make it easy to implement [new algorithm

  ideas](https://surprise.readthedocs.io/en/stable/building_custom_algo.html).

- Provide tools to [evaluate](https://surprise.readthedocs.io/en/stable/model_selection.html),

  [analyse](https://nbviewer.jupyter.org/github/NicolasHug/Surprise/tree/master/examples/notebooks/KNNBasic_analysis.ipynb/)

  and

  [compare](https://nbviewer.jupyter.org/github/NicolasHug/Surprise/blob/master/examples/notebooks/Compare.ipynb)

  the algorithms' performance. Cross-validation procedures can be run very

  easily using powerful CV iterators (inspired by

  [scikit-learn](https://scikit-learn.org/) excellent tools), as well as

  [exhaustive search over a set of

  parameters](https://surprise.readthedocs.io/en/stable/getting_started.html#tune-algorithm-parameters-with-gridsearchcv).

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](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVD)

algorithm.

```python

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)

```

**Output**:

```

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

                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     

RMSE (testset)    0.9367  0.9355  0.9378  0.9377  0.9300  0.9355  0.0029  

MAE (testset)     0.7387  0.7371  0.7393  0.7397  0.7325  0.7375  0.0026  

Fit time          0.62    0.63    0.63    0.65    0.63    0.63    0.01    

Test time         0.11    0.11    0.14    0.14    0.14    0.13    0.02    

```

[Surprise](https://surpriselib.com) can do **much** more (e.g,

[GridSearchCV](https://surprise.readthedocs.io/en/stable/getting_started.html#tune-algorithm-parameters-with-gridsearchcv))!

You'll find [more usage

examples](https://surprise.readthedocs.io/en/stable/getting_started.html) in the

[documentation ](https://surprise.readthedocs.io/en/stable/index.html).

Benchmarks

----------

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](https://grouplens.org/datasets/movielens/) 100k and

1M datasets. The folds are the same for all the algorithms. All experiments are

run on a laptop with an intel i5 11th Gen 2.60GHz. The code

for generating these tables can be found in the [benchmark

example](https://github.com/NicolasHug/Surprise/tree/master/examples/benchmark.py).

| [Movielens 100k](http://grouplens.org/datasets/movielens/100k)                                                                         |   RMSE |   MAE | Time    |

|:---------------------------------------------------------------------------------------------------------------------------------------|-------:|------:|:--------|

| [SVD](http://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVD)      |  0.934 | 0.737 | 0:00:06 |

| [SVD++ (cache_ratings=False)](http://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVDpp)  |  0.919 | 0.721 | 0:01:39 |

| [SVD++ (cache_ratings=True)](http://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVDpp)  |  0.919 | 0.721 | 0:01:22 |

| [NMF](http://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.NMF)      |  0.963 | 0.758 | 0:00:06 |

| [Slope One](http://surprise.readthedocs.io/en/stable/slope_one.html#surprise.prediction_algorithms.slope_one.SlopeOne)                 |  0.946 | 0.743 | 0:00:09 |

| [k-NN](http://surprise.readthedocs.io/en/stable/knn_inspired.html#surprise.prediction_algorithms.knns.KNNBasic)                        |  0.98  | 0.774 | 0:00:08 |

| [Centered k-NN](http://surprise.readthedocs.io/en/stable/knn_inspired.html#surprise.prediction_algorithms.knns.KNNWithMeans)           |  0.951 | 0.749 | 0:00:09 |

| [k-NN Baseline](http://surprise.readthedocs.io/en/stable/knn_inspired.html#surprise.prediction_algorithms.knns.KNNBaseline)            |  0.931 | 0.733 | 0:00:13 |

| [Co-Clustering](http://surprise.readthedocs.io/en/stable/co_clustering.html#surprise.prediction_algorithms.co_clustering.CoClustering) |  0.963 | 0.753 | 0:00:06 |

| [Baseline](http://surprise.readthedocs.io/en/stable/basic_algorithms.html#surprise.prediction_algorithms.baseline_only.BaselineOnly)   |  0.944 | 0.748 | 0:00:02 |

| [Random](http://surprise.readthedocs.io/en/stable/basic_algorithms.html#surprise.prediction_algorithms.random_pred.NormalPredictor)    |  1.518 | 1.219 | 0:00:01 |

| [Movielens 1M](https://grouplens.org/datasets/movielens/1m)                                                                             |   RMSE |   MAE | Time    |

|:----------------------------------------------------------------------------------------------------------------------------------------|-------:|------:|:--------|

| [SVD](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVD)      |  0.873 | 0.686 | 0:01:07 |

| [SVD++ (cache_ratings=False)](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVDpp)  |  0.862 | 0.672 | 0:41:06 |

| [SVD++ (cache_ratings=True)](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVDpp)  |  0.862 | 0.672 | 0:34:55 |

| [NMF](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.NMF)      |  0.916 | 0.723 | 0:01:39 |

| [Slope One](http://surprise.readthedocs.io/en/stable/slope_one.html#surprise.prediction_algorithms.slope_one.SlopeOne)                 |  0.907 | 0.715 | 0:02:31 |

| [k-NN](http://surprise.readthedocs.io/en/stable/knn_inspired.html#surprise.prediction_algorithms.knns.KNNBasic)                        |  0.923 | 0.727 | 0:05:27 |

| [Centered k-NN](http://surprise.readthedocs.io/en/stable/knn_inspired.html#surprise.prediction_algorithms.knns.KNNWithMeans)           |  0.929 | 0.738 | 0:05:43 |

| [k-NN Baseline](http://surprise.readthedocs.io/en/stable/knn_inspired.html#surprise.prediction_algorithms.knns.KNNBaseline)            |  0.895 | 0.706 | 0:05:55 |

| [Co-Clustering](http://surprise.readthedocs.io/en/stable/co_clustering.html#surprise.prediction_algorithms.co_clustering.CoClustering) |  0.915 | 0.717 | 0:00:31 |

| [Baseline](http://surprise.readthedocs.io/en/stable/basic_algorithms.html#surprise.prediction_algorithms.baseline_only.BaselineOnly)   |  0.909 | 0.719 | 0:00:19 |

| [Random](http://surprise.readthedocs.io/en/stable/basic_algorithms.html#surprise.prediction_algorithms.random_pred.NormalPredictor)    |  1.504 | 1.206 | 0:00:19 |

Installation

------------

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

    $ 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](https://cython.org/) and

[numpy](https://www.numpy.org/)):

    $ git clone https://github.com/NicolasHug/surprise.git

    $ cd surprise

    $ pip install .

License and reference

---------------------

This project is licensed under the [BSD

3-Clause](https://opensource.org/licenses/BSD-3-Clause) license, so it can be

used for pretty much everything, including commercial applications.

I'd love to know how Surprise is useful to you. Please don't hesitate to open

an issue and describe how you use it!

Please make sure to cite the

[paper](https://joss.theoj.org/papers/10.21105/joss.02174) if you use

Surprise for your research:

    @article{Hug2020,

      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}

    }

Contributors

------------

The following persons have contributed to [Surprise](https://surpriselib.com):

ashtou, Abhishek Bhatia, bobbyinfj, caoyi, Chieh-Han Chen,  Raphael-Dayan, 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, lapidshay, Hengji Liu, Ravi Makhija, Maher Malaeb, Manoj K, James

McNeilis, Naturale0, nju-luke, Pierre-Louis Pécheux, Jay Qi, Lucas Rebscher,

Craig Rodrigues, Skywhat, Hercules Smith, David Stevens, Vesna Tanko,

TrWestdoor, Victor Wang, Mike Lee Williams, Jay Wong, Chenchen Xu, YaoZh1918.

Thanks a lot :) !

Development Status

------------------

Starting from version 1.1.0 (September 2019), I will only maintain the package,

provide bugfixes, and perhaps sometimes perf improvements. I have less time to

dedicate to it now, so I'm unabe to consider new features.

For bugs, issues or questions about [Surprise](https://surpriselib.com), please

avoid sending me emails; I will most likely not be able to answer). Please use

the GitHub [project page](https://github.com/NicolasHug/Surprise) instead, so

that others can also benefit from it.