Python-based Recommendation Platform

An Python-based Recommendation Platform is a recommendation platform that is an 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):
  • a Python/Spark-based Recommender.
  • a Python SciKit-Surprise-based Recommender.
• Counter-Example(s):
  • an R-based Recommender.
• See: Item Recommendations Task, Item Recommendations Method.

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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.

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