Crab as known as scikits.recommender is a Python framework for building recommender engines that integrate with the world of scientific Python packages (numpy, scipy, matplotlib), provide a rich set of components from which user can construct a customized recommender system from a set of algorithms and be usable in various contexts: ** science and engineering ** .
Customer Reviews
Haru K.
Advanced user of CrabIt is one of the best available open source customized recommender engine builder in python.
There are many points to mention from easy API to robust behavior but I really like one feature about it, the pre-loaded datasets. It might not sound to everyone that I am calling it really good feature but if you realise these helps one go hands-on without any delay. One can just pip install the library and start playing. These very much helps in learning phase.
One thing I really don't like about Crab is the documentation. The documentation is never ever updated and actually the present documentation is really poor. The contributors to Crab should take the step and write a proper documentation.
I recommend not to rely on the original documentation as it is not maintained properly and don't have sufficient information. I better suggest to opt for different implementations available in your learning pipeline. Once you are used to API you can easily build your own recommender engine in no time.
Crab is meant to build recommender engines. At ML Hub we have number of clients that come with a definition to build customized recommender engines, for example: e-commerce websites, online book stores and many more. So we use Crab to develop such recommender engines.
We rely on it because it has the easiest API and also there is no complex flow while building a recommender engine. It can be built in few lines of code (pre-processing not considered)
