Introduction to Crab - Python Framework for Building Recommender Systems
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Crab is an open-source Python framework designed for building recommendation engines, offering collaborative, content, and hybrid filtering alternatives to Apache Mahout. It was initiated in April 2011 by a community-driven team aiming to integrate it into the scikit ecosystem and optimize it for scientific calculations. The framework supports various algorithms, metrics for evaluating recommender performance, and integrates with tools like Hadoop for larger computations.
Introduction to Crab - Python Framework for Building Recommender Systems
- 1. Crab A Python Framework for Building Recommendation Engines Scipy 2011, Austin TX Marcel Caraciolo Ricardo Caspirro Bruno Melo @marcelcaraciolo @ricardocaspirro @brunomelo 1
- 2. What is Crab ? A python framework for building recommendation engines A Scikit module for collaborative, content and hybrid filtering Mahout Alternative for Python Developers :D Open-Source under the BSD license https://github.com/muricoca/crab 2
- 3. When started ? It began one year ago Community-driven, 4 members Since April,2011 the open-source labs Muriçoca incorporated it Since April,2011 rewritting it as Scikit https://github.com/muricoca/ 3
- 4. Knowing Scikits Scikits are Scipy Toolkits - independent and projects hosted under a common namespace. Scikits Image Scikits MlabWrap Scikits AudioLab Scikit Learn .... http://scikits.appspot.com/scikits 4
- 5. Knowing Scikits Scikit-Learn Machine Learning Algorithms + scientific Python packages (Numpy, Scipy and Matplotlib) http://scikit-learn.sourceforge.net/ Our goal: Incorporate the Crab as Scikit and incorporate some parts of them at Scikit-learn 5
- 6. Why Recommendations ? The world is an over-crowded place !"#$%&'()$*+$,-$&.#'/0'&%)#)$1(,0# 6
- 7. Why Recommendations * +,&-.$/).#&0#/"1.#$%234(".# ? $/)#5(&6 7&.2.#"$4,#)$8 We are overloaded * 93((3&/.#&0#:&'3".;#5&&<.# $/)#:-.34#2%$4<.#&/(3/" Thousands of news articles and blog posts each day * =/#>$/&3;#?#@A#+B#4,$//"(.;# 2,&-.$/).#&0#7%&6%$:.# Millions of movies, books and music tracks online "$4,#)$8 Several Places, Offers and Events * =/#C"1#D&%<;#."'"%$(# Even Friends sometimes we are overloaded ! 2,&-.$/).#&0#$)#:"..$6".# ."/2#2&#-.#7"%#)$8 7
- 8. Why Recommendations ? We really need and consume only a few of them! “A lot of times, people don’t know what they want until you show it to them.” Steve Jobs “We are leaving the Information age, and entering into the Recommendation age.” Chris Anderson, from book Long Tail 8
- 9. Why Recommendations ? Can Google help ? Yes, but only when we really know what we are looking for But, what’s does it mean by “interesting” ? Can Facebook help ? Yes, I tend to find my friends’ stuffs interesting What if i had only few friends and what they like do not always attract me ? Can experts help ? Yes, but it won’t scale well. But it is what they like, not me! Exactly same advice! 9
- 10. Why Recommendations ? Recommendation Systems Systems designed to recommend to me something I may like 10
- 11. Why Recommendations ? !"#$%&"'$"'(')*#*+,) Recommendation Systems -+*#)+. -#/') 0#)1# ! 2' 23&4"+')1 5,6 7),*%'"&863 Graph Representation 11
- 12. The current Crab Collaborative Filtering algorithms User-Based, Item-Based and Slope One Evaluation of the Recommender Algorithms Precision, Recall, F1-Score, RMSE Precision-Recall Charts 12
- 13. The current Crab Precision-Recall Charts 13
- 14. The current Crab 14
- 15. The current Crab Using REST APIs to deploy the recommender django-piston, django-rest, django-tastypie 15
- 16. Crab is already in production Brazilian Social Network called Atepassar.com Educational network with more than 60.000 students and 3000 video-classes Running on Python + Numpy + Scipy and Django Backend for Recommendations MongoDB - mongoengine Daily Recommendations with Explanations 16
- 17. Evaluating your recommender Crab implements the most used recommender metrics. Precision, Recall, F1-Score, RMSE Using matplotlib for a plotter utility Implement new metrics Simulations support maybe (??) 17
- 18. Evaluating your recommender All you have to do is implement your Evaluator 18
- 19. Distributing the recommendation computations Use Hadoop and Map-Reduce intensively Investigating the Yelp mrjob framework https://github.com/pfig/mrjob Develop the Netflix and novel standard-of-the-art used Matrix Factorization, Singular Value Decomposition (SVD), Boltzman machines The most commonly used is Slope One technique. 19
- 20. Why migrate ? Old Crab running only using Pure Python Recommendations demand heavy maths calculations and lots of processing Compatible with Numpy and Scipy libraries High Standard and popular scientific libraries optimized for scientific calculations in Python Scikits projects are amazing! Active Communities, Scientific Conferences and updated projects (e.g. scikit-learn) Turn the Crab framework visible for the community Join the scientific researchers and machine learning developers around the Globe coding with Python to help us in this project Be Fast and Furious 20
- 21. Why migrate ? Numpy optimized with PyPy 2.x - 48.x Faster http://morepypy.blogspot.com/2011/05/numpy-in-pypy-status-and-roadmap.html 21
- 22. How are we working ? Sprints, Online Discussions and Issues https://github.com/muricoca/crab/wiki/UpcomingEvents 22
- 23. Future Releases Planned Release 0.1 Collaborative Filtering Algorithms working, sample datasets to load and test Planned Release 0.11 Evaluation of Recommendation Algorithms and Database Models support Planned Release 0.12 Recommendation as Services with REST APIs .... 23
- 24. Join us! 1. Read our Wiki Page https://github.com/muricoca/crab/wiki/Developer-Resources 2. Check out our current sprints and open issues https://github.com/muricoca/crab/issues 3. Forks, Pull Requests mandatory 4. Join us at irc.freenode.net #muricoca or at our discussion list http://groups.google.com/group/scikit-crab 24
- 25. Crab A Python Framework for Building Recommendation Engines https://github.com/muricoca/crab Marcel Caraciolo Ricardo Caspirro Bruno Melo @marcelcaraciolo @ricardocaspirro @brunomelo {marcel, ricardo,bruno}@muricoca.com 25