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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: Give users perfect control over their experiments.
- Overview
- Getting Started, Example
- Benchmarks
- Installation
- License and Reference
- Contributors
- Development Status
Surprise is a Pythonscikitfor building and analyzingrecommender systems that deal with explicit rating data. Surprise was designed with thefollowing purposes in mind: 1. Give users perfect control over their experiments. To this end, a strongemphasis is laid ondocumentation, which wehave tried to make as clear and precise as possible by pointing ou...
Here is a simple example showing how you can (down)load a dataset, split it for5-fold cross-validation, and compute the MAE and RMSE of theSVDalgorithm. Output: Surprise can do much more (e.g,GridSearchCV)!You'll find more usageexamples in thedocumentation .
Here are the average RMSE, MAE and total execution time of various algorithms(with their default parameters) on a 5-fold cross-validation procedure. Thedatasets are the Movielens 100k and1M datasets. The folds are the same for all the algorithms. All experiments arerun on a laptop with an intel i5 11th Gen 2.60GHz. The codefor generating these tabl...
With pip (you'll need numpy, and a C compiler. Windowsusers might prefer using conda): With conda: For the latest version, you can also clone the repo and build the source(you'll first need Cython andnumpy):
This project is licensed under the BSD3-Clauselicense, so it can beused for pretty much everything, including commercial applications. I'd love to know how Surprise is useful to you. Please don't hesitate to openan issue and describe how you use it! Please make sure to cite thepaperif you useSurprise for your research:
The following persons have contributed to Surprise: ashtou, Abhishek Bhatia, bobbyinfj, caoyi, Chieh-Han Chen, Raphael-Dayan, ОлегДемиденко, 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,...
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 todedicate to it now, so I'm unabe to consider new features. For bugs, issues or questions about Surprise, pleaseavoid sending me emails; I will most likely not be able to answer). Please usethe ...
Surprise Package is a 1960 American comedy film directed by Stanley Donen and starring Yul Brynner, Mitzi Gaynor, and Noël Coward. The supporting cast features George Coulouris, Michael Balfour, Eric Pohlmann and Barry Foster.
Surprise is an easy-to-use Python scikit for recommender systems. If you’re new to Surprise, we invite you to take a look at the Getting Started guide, where you’ll find a series of tutorials illustrating all you can do with Surprise. You can also check out the FAQ for many use-case example.
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: Give users perfect control over their experiments.
Oct 24, 2021 · The Surprise Package. Surprise is a Python module that allows you to create and test rate prediction systems. It was created to closely resemble the scikit-learn API, which users familiar with the Python machine learning ecosystem should be comfortable with. Surprise includes a set of estimators (or prediction algorithms) for evaluating ...
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Basic usage. Automatic cross-validation. Surprise has a set of built-in algorithms and datasets for you to play with. In its simplest form, it only takes a few lines of code to run a cross-validation procedure: From file examples/basic_usage.py.
