From add07d91ca89a968cc8faec26e02cc16a42fbb6e Mon Sep 17 00:00:00 2001 From: Vinta Date: Thu, 25 May 2017 02:55:25 +0800 Subject: [PATCH] add Recommender Systems section --- README.md | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 84b2a946..80bc52cb 100644 --- a/README.md +++ b/README.md @@ -67,6 +67,7 @@ Inspired by [awesome-php](https://github.com/ziadoz/awesome-php). - [Permissions](#permissions) - [Processes](#processes) - [Queue](#queue) + - [Recommender Systems](#recommender-systems) - [RESTful API](#restful-api) - [RPC Servers](#rpc-servers) - [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).* * [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. * [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. -* [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/). * [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. * [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 *Libraries for developing RESTful APIs.*