{"repo":"NicolasHug/Surprise","free":true,"listed":false,"github":"https://github.com/NicolasHug/Surprise","clone":"git clone https://github.com/NicolasHug/Surprise.git","description":"A Python scikit for building and analyzing recommender systems","language":"Python","stars":6806,"topics":["recommender","systems","recommendation","svd","matrix","factorization","machine-learning"],"license":"BSD-3-Clause","category":"machine-learning","readme_excerpt":"Overview -------- 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. 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 detail of the algorithms. - Alleviate the pain of Dataset handling. Users can use both built-in datasets (Movielens, Jester), and their own custom datasets. - Provide various ready-to-use prediction algorithms such as baseline algorithms, neighborhood methods, matrix factorization-based ( SVD, PMF, SVD++, NMF), and many others. Also, various similarity measures (cosine, MSD, pearson...) are built-in. - Make it easy to implement new algorithm ideas. - Provide tools to evaluate, analyse and compare the algorithms' performance. Cross-validation procedures can be run very easily using powerful CV iterators (inspired by scikit-learn excellent tools), as well as exhaustive search over a set of parameters. 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. Output : Surprise can do","default_branch":null,"files":null,"tree":[],"storefront":"/r/NicolasHug","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/NicolasHug/Surprise/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}