Surprise is a Python scikit building and analyzing recommender systems. 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.
Keywords for this software
References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Rago, Antonio; Cocarascu, Oana; Bechlivanidis, Christos; Lagnado, David; Toni, Francesca: Argumentative explanations for interactive recommendations (2021)
- Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, Tommaso Di Noia: Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation (2021) arXiv
- Rohan Anand, Joeran Beel: Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization (2020) arXiv
- Salah, Aghiles; Truong, Quoc-Tuan; Lauw, Hady W.: Cornac: a comparative framework for multimodal recommender systems (2020)
- Shuai Zhang, Yi Tay, Lina Yao, Bin Wu, Aixin Sun: DeepRec: An Open-source Toolkit for Deep Learning based Recommendation (2019) arXiv