Difference between revisions of "Crab"

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(Crab: A Python Framework for Building Recommender Engines)
 
 
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'''Crab''' is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (Numpy, Scipy , Matplotlib). The engine aims primarily to provide a rich set of components from which you can construct a customized recommender system from a set of algorithms. It is designed for scability, flexibility and performance making use of scientific optimized python packages in order to provide simple and efficient solutions pluggable that are accessible to everybody and reusable in various contexts: science and engineering.   
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'''Crab''' is a flexible, fast recommender engine for [[Python]] that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (Numpy, Scipy , Matplotlib). The engine aims primarily to provide a rich set of components from which you can construct a customized recommender system from a set of algorithms. It is designed for scability, flexibility and performance making use of scientific optimized python packages in order to provide simple and efficient solutions pluggable that are accessible to everybody and reusable in various contexts: science and engineering.   
  
 
== Concept ==
 
== Concept ==
  
The engine is open-source by BSD license and takes user's preferences for items and returns estimated preferences for other items. For instance, a web site that sells movies could easily use Crab to figure out, from past purchase data, which movies a customer might be interested in watching to. Technically, Crab would work as a toolkit with several recommender algorithms and extensible interfaces as also with  support to data models such as databases, text files, etc. The output can be easily in a future release provided by web services via REST or SOAP. Another important feature is to give the machine learning developer to evaluate by using perfomance metrics widely used in the recommenders field as well as to implement their custom recommender algorithms by using Crab's base interfaces.
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The engine is open-source by BSD license and takes user's preferences for items and returns estimated preferences for other items. For instance, a web site that sells movies could easily use Crab to figure out, from past purchase data, which movies a customer might be interested in watching to. Technically, Crab would work as a toolkit with several recommender algorithms and extensible interfaces as also with  support to data models such as databases, text files, etc. The output can be easily in a future release provided by web services via REST or SOAP. Another important feature is to give the machine learning developer the tools for evaluate the techniques by using perfomance metrics widely used in the recommenders field as well as providing the base interfaces in order to implement custom recommender algorithms.
  
  
 
== Currently in Development ==
 
== Currently in Development ==
  
* Support for Data Models (text files and Dictionary Models)
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*Collaborative Filtering methods: Item-Based and User-Based.
* Support for commonly used pairwise metrics such as Cosine, Pearson, Euclidean, Tanimoto, etc.
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* Support to FileDataModels
* Base interfaces for Similarities and Recommenders
 
* Collaborative Filtering methods: Item-Based and User-Based.
 
 
 
  
 
== External links ==
 
== External links ==
 
* https://github.com/muricoca/crab official website
 
* https://github.com/muricoca/crab official website
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* http://www.slideshare.net/marcelcaraciolo/crab-a-python-framework-for-building-recommendation-systems  Presentation about Crab
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* http://www.archive.org/details/Thursday-203-1-CrabARecommendationEngineFrameworkForPython  Video Lecture about Crab at Scipy Conference 2011
  
 
[[Category:Software]]
 
[[Category:Software]]

Latest revision as of 15:08, 3 February 2012

Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (Numpy, Scipy , Matplotlib). The engine aims primarily to provide a rich set of components from which you can construct a customized recommender system from a set of algorithms. It is designed for scability, flexibility and performance making use of scientific optimized python packages in order to provide simple and efficient solutions pluggable that are accessible to everybody and reusable in various contexts: science and engineering.

Concept

The engine is open-source by BSD license and takes user's preferences for items and returns estimated preferences for other items. For instance, a web site that sells movies could easily use Crab to figure out, from past purchase data, which movies a customer might be interested in watching to. Technically, Crab would work as a toolkit with several recommender algorithms and extensible interfaces as also with support to data models such as databases, text files, etc. The output can be easily in a future release provided by web services via REST or SOAP. Another important feature is to give the machine learning developer the tools for evaluate the techniques by using perfomance metrics widely used in the recommenders field as well as providing the base interfaces in order to implement custom recommender algorithms.


Currently in Development

  • Collaborative Filtering methods: Item-Based and User-Based.
  • Support to FileDataModels

External links