Python-based Recommendation Platform: Difference between revisions
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* https://pypi.python.org/pypi/scikit-surprise | * https://pypi.python.org/pypi/scikit-surprise | ||
** QUOTE: [[Python Surprise scikit|Surprise]] is a [[Python scikit]] for [[building recommender systems|building]] and [[analyzing recommender | ** QUOTE: [[Python Surprise scikit|Surprise]] is a [[Python scikit]] for [[building recommender systems|building]] and [[analyzing recommender system]]s. <P> 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. | *** 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. | *** Alleviate the pain of [[Dataset handling]]. Users can use both built-in datasets ([[Movielens]], [[Jester]]), and their own custom datasets. | ||
Latest revision as of 21:12, 9 May 2024
An Python-based Recommendation Platform is a recommendation platform that is a Python-based system.
- Context:
- It can range from being a Demo Python-based Recommendation Platform to being a Production Python-based Recommendation Platform.
- Example(s):
- Counter-Example(s):
- See: Item Recommendations Task, Item Recommendations Method.
References
2016
- https://pypi.python.org/pypi/scikit-surprise
- QUOTE: Surprise is a Python scikit for 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, as well as exhaustive search over a set of parameters.
- QUOTE: Surprise is a Python scikit for building and analyzing recommender systems.