RecSysWiki - User contributions [en]

List of recommender system blogs

2011-11-24T17:34:41Z

Marcelcaraciolo:

Blogs by individuals:
* [http://bickson.blogspot.com/ Large Scale Machine Learning and Other Animals], [[Danny Bickson]], main author of the [[GraphLab]] [[collaborative filtering]] library
* [http://urbanmining.wordpress.com/ Urban Mining], [[Neal Lathia]]
* [http://musicmachinery.com/ Music Machinery]], [[Paul Lamere]] of [[The Echo Nest]]
* [http://ssc.io/ "I, for one, welcome our new computer overlords."], [[Sebastian Schelter]], [[Apache Mahout]] contributor
* [http://technocalifornia.blogspot.com/ TechnoCalifornia], [[Xavier Amatriain]] of [[Netflix]]
* [http://glinden.blogspot.com/ Geeking with Greg], [[Greg Linden]], developer of the first [[Amazon]] recommender
* [http://aimotion.blogspot.com/ A.I in Motion], [[Marcel Caraciolo]], main author of the Python Recommender Framework [[Crab]]

Company blogs:
* [http://tech.hulu.com/blog/ Hulu Tech blog], by [[Hulu]]

Other blogs:
* [http://acmrecsys.wordpress.com/ ACM RecSys blog]

[[Category: List|Blog]]

Crab

2011-07-30T04:43:15Z

Marcelcaraciolo: /* External links */

'''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 ==
* https://github.com/muricoca/crab official website
* http://www.slideshare.net/marcelcaraciolo/crab-a-python-framework-for-building-recommendation-systems Presentation about Crab
* http://www.archive.org/details/Thursday-203-1-CrabARecommendationEngineFrameworkForPython Video Lecture about Crab at Scipy Conference 2011

[[Category:Software]]

Crab

2011-07-30T04:36:39Z

Marcelcaraciolo: /* Currently in Development */

Crab

2011-06-09T19:27:29Z

Marcelcaraciolo:

'''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 ==

* Support for Data Models (text files and Dictionary Models)
* Support for commonly used pairwise metrics such as Cosine, Pearson, Euclidean, Tanimoto, etc.
* Base interfaces for Similarities and Recommenders
* Collaborative Filtering methods: Item-Based and User-Based.

== External links ==
* https://github.com/muricoca/crab official website
* http://www.slideshare.net/marcelcaraciolo/crab-a-python-framework-for-building-recommendation-systems Presentation about Crab

[[Category:Software]]

Crab

2011-06-09T19:05:17Z

Marcelcaraciolo:

'''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 ==

* Support for Data Models (text files and Dictionary Models)
* Support for commonly used pairwise metrics such as Cosine, Pearson, Euclidean, Tanimoto, etc.
* Base interfaces for Similarities and Recommenders
* Collaborative Filtering methods: Item-Based and User-Based.

== External links ==
* https://github.com/muricoca/crab official website

[[Category:Software]]

Crab

2011-06-09T18:57:53Z

Marcelcaraciolo: Crab: A Python Framework for Building Recommender Engines

'''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 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.

== Currently in Development ==

* Support for Data Models (text files and Dictionary Models)
* Support for commonly used pairwise metrics such as Cosine, Pearson, Euclidean, Tanimoto, etc.
* Base interfaces for Similarities and Recommenders
* Collaborative Filtering methods: Item-Based and User-Based.

== External links ==
* https://github.com/muricoca/crab official website

[[Category:Software]]