推荐系统

 花了大概1天的时间整理的各语言的开源推荐系统，比较完整全面的项目标红。

一、python库

1、benfred/implicit

Fast Python Collaborative Filtering for Implicit Datasets

https://github.com/benfred/implicit

2、Mendeley/mrec

A recommender systems development and evaluation package by Mendeley

https://github.com/mendeley/mrec       

3、lyst/lightfm

A Python implementation of LightFM, a hybrid recommendation algorithm.

https://github.com/lyst/lightfm

4、MrChrisJohnson/logistic-mf

Logistic Matrix Factorization for Implicit Feedback Data.

https://github.com/MrChrisJohnson/logistic-mf

5、NicolasHug/Surprise

A Python scikit for building and analyzing recommender systems

https://github.com/NicolasHug/Surprise

6、ocelma/python-recsys

A python library for implementing a recommender system

https://github.com/ocelma/python-recsys

7、muricoca/crab

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).

https://github.com/muricoca/crab

8、python-recsys/crab

Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (python, numpy, scipy, matplotlib)

https://github.com/python-recsys/crab

9、ibayer/fastFM

fastFM: A Library for Factorization Machines

https://github.com/ibayer/fastFM

10、jadianes/winerama-recommender-tutorial

A wine recommender system tutorial using Python technologies such as Django, Pandas, or Scikit-learn, and others such as Bootstrap.

https://github.com/jadianes/winerama-recommender-tutorial

二、java库

1、lenskit/lenskit

LensKit recommender toolkit

https://github.com/lenskit/lenskit

2、apache/mahout

The Apache Mahout™ project's goal is to build an environment for quickly creating scalable performant machine learning applications.

https://github.com/apache/mahout

3、myrrix/myrrix-recommender

Stand-alone recommender system from Myrrix

https://github.com/myrrix/myrrix-recommender

4、easyrec

Add recommendations to your website

http://easyrec.org/

5、SeldonIO/seldon-server

Enterprise machine learning platform for prediction and recommendation.

https://github.com/SeldonIO/seldon-server

6、rapidminer

RapidMiner makes data science teams more productive through a unified platform for data prep, machine learning, and model deployment.

https://rapidminer.com/

7、Duine Framework

a Java based recommendation system

https://sourceforge.net/projects/duine/

8、guoguibing/librec

LibRec: A Leading Java Library for Recommender Systems

https://github.com/guoguibing/librec/

9、RankSys/RankSys

Java 8 Recommender Systems framework for novelty, diversity and much more

https://github.com/RankSys/RankSys

10、learning-layers/TagRec

Towards A Standardized Tag Recommender Benchmarking Framework

https://github.com/learning-layers/TagRec

11、recommenders/rival

RiVal recommender system evaluation toolkit

https://github.com/recommenders/rival/

12、OryxProject/oryx

Oryx 2: Lambda architecture on Apache Spark, Apache Kafka for real-time large scale machine learning

https://github.com/OryxProject/oryx

13、Waikato/moa

MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.

https://github.com/Waikato/moa

三、C++库

1、Gnnng/SVDFeature

A recommend system I used before. The official website is http://svdfeature.apexlab.org/wiki/Ma

https://github.com/Gnnng/SVDFeature

2、cjlin1/libmf

LIBMF is a library for large-scale sparse matrix factorization. For the optimization problem it solves

https://github.com/cjlin1/libmf

3、srendle/libfm

Library for factorization machines

https://github.com/srendle/libfm

4、mikegashler/waffles

A toolkit of machine learning algorithms.

https://github.com/mikegashler/waffles

5、yixuan/recosystem

Recommender System Using Parallel Matrix Factorization

https://github.com/yixuan/recosystem

四、其他

1、apache/incubator-predictionio

PredictionIO, a machine learning server for developers and ML engineers. Built on Apache Spark, HBase and Spray.

https://github.com/apache/incubator-predictionio

2、guymorita/recommendationRaccoon

A collaborative filtering based recommendation engine and NPM module built on top of Node.js and Redis. The engine uses the Jaccard coefficient to determine the similarity between users and k-nearest-neighbors to create recommendations. This module is useful for anyone with a database of users, a database of products/movies/items and the desire …

https://github.com/guymorita/recommendationRaccoon

3、DataSystemsLab/recdb-postgresql

RecDB is a recommendation engine built entirely inside PostgreSQL

https://github.com/DataSystemsLab/recdb-postgresql

4、zenogantner/MyMediaLite

recommender system library for the CLR (.NET)

https://github.com/zenogantner/MyMediaLite

5、crowdrec/idomaar

Idomaar is the CrowdRec recommendation and evaluation reference framework.

https://github.com/crowdrec/idomaar/

6、grahamjenson/hapiger

HapiGer is an http-wrapper around the Good Enough Recommendation engine using the Hapi.js framework

https://github.com/grahamjenson/hapiger

7、OndraFiedler/spark-recommender

Scalable recommendation system written in Scala using the Apache Spark framework

https://github.com/OndraFiedler/spark-recommender