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add Recommender Systems section

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@@ -67,6 +67,7 @@ Inspired by [awesome-php](https://github.com/ziadoz/awesome-php).
- [Permissions](#permissions) - [Permissions](#permissions)
- [Processes](#processes) - [Processes](#processes)
- [Queue](#queue) - [Queue](#queue)
- [Recommender Systems](#recommender-systems)
- [RESTful API](#restful-api) - [RESTful API](#restful-api)
- [RPC Servers](#rpc-servers) - [RPC Servers](#rpc-servers)
- [Science](#science) - [Science](#science)
@@ -744,11 +745,10 @@ Inspired by [awesome-php](https://github.com/ziadoz/awesome-php).
*Libraries for Machine Learning. See: [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning#python).* *Libraries for Machine Learning. See: [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning#python).*
* [gensim](https://github.com/RaRe-Technologies/gensim) - Topic Modelling for Humans. * [gensim](https://github.com/RaRe-Technologies/gensim) - Topic Modelling for Humans.
* [LightFM](https://github.com/lyst/lightfm) - A Python implementation of a number of popular recommendation algorithms. * [Metrics](https://github.com/dmlc/xgboost) - Machine learning evaluation metrics.
* [NuPIC](https://github.com/numenta/nupic) - Numenta Platform for Intelligent Computing. * [NuPIC](https://github.com/numenta/nupic) - Numenta Platform for Intelligent Computing.
* [scikit-learn](http://scikit-learn.org/) - The most popular Python library for Machine Learning. * [scikit-learn](http://scikit-learn.org/) - The most popular Python library for Machine Learning.
* [Spark ML](http://spark.apache.org/docs/latest/ml-guide.html) - [Apache Spark](http://spark.apache.org/)'s scalable Machine Learning library. * [Spark ML](http://spark.apache.org/docs/latest/ml-guide.html) - [Apache Spark](http://spark.apache.org/)'s scalable Machine Learning library.
* [surprise](http://surpriselib.com) - A scikit for building and analyzing recommender systems.
* [vowpal_porpoise](https://github.com/josephreisinger/vowpal_porpoise) - A lightweight Python wrapper for [Vowpal Wabbit](https://github.com/JohnLangford/vowpal_wabbit/). * [vowpal_porpoise](https://github.com/josephreisinger/vowpal_porpoise) - A lightweight Python wrapper for [Vowpal Wabbit](https://github.com/JohnLangford/vowpal_wabbit/).
* [xgboost](https://github.com/dmlc/xgboost) - A scalable, portable, and distributed gradient boosting library. * [xgboost](https://github.com/dmlc/xgboost) - A scalable, portable, and distributed gradient boosting library.
@@ -894,6 +894,16 @@ Inspired by [awesome-php](https://github.com/ziadoz/awesome-php).
* [rq](http://python-rq.org/) - Simple job queues for Python. * [rq](http://python-rq.org/) - Simple job queues for Python.
* [simpleq](https://github.com/rdegges/simpleq) - A simple, infinitely scalable, Amazon SQS based queue. * [simpleq](https://github.com/rdegges/simpleq) - A simple, infinitely scalable, Amazon SQS based queue.
## Recommender Systems
*Libraries for building recommender systems*
* [annoy](https://github.com/spotify/annoy) - Approximate Nearest Neighbors in C++/Python optimized for memory usage.
* [fastFM](https://github.com/ibayer/fastFM) - A library for Factorization Machines.
* [implicit](https://github.com/benfred/implicit) - A fast Python implementation of collaborative filtering for implicit datasets.
* [LightFM](https://github.com/lyst/lightfm) - A Python implementation of a number of popular recommendation algorithms.
* [surprise](http://surpriselib.com) - A scikit for building and analyzing recommender systems.
## RESTful API ## RESTful API
*Libraries for developing RESTful APIs.* *Libraries for developing RESTful APIs.*