librec - LibRec: A Leading Java Library for Recommender Systems, see

•    Java

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LibRec (http://www.librec.net) is a Java library for recommender systems (Java version 1.7 or higher required). It implements a suit of state-of-the-art recommendation algorithms, aiming to resolve two classic recommendation tasks: rating prediction and item ranking. A movie recommender system is designed and available here.

recommender-systems recommendation-algorithms collaborative-filtering matrix-factorization tensor-factorization probabilistic-graphical-models recommender systems factorization matrix tensor collaborative filtering sparse

Surprise - A Python scikit for building and analyzing recommender systems

•    Python

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Surprise is a Python scikit building and analyzing recommender systems that deal with explicit rating data. The name SurPRISE (roughly :) ) stands for Simple Python RecommendatIon System Engine.

recommender systems recommendation svd matrix factorization

lightfm - A Python implementation of LightFM, a hybrid recommendation algorithm.

•    Python

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LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback, including efficient implementation of BPR and WARP ranking losses. It's easy to use, fast (via multithreaded model estimation), and produces high quality results. It also makes it possible to incorporate both item and user metadata into the traditional matrix factorization algorithms. It represents each user and item as the sum of the latent representations of their features, thus allowing recommendations to generalise to new items (via item features) and to new users (via user features).

machine-learning recommender matrix-factorization learning-to-rank recommender-system

Alink - Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform

•    Java

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Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.

machine-learning data-mining statistics kafka graph-algorithms clustering word2vec regression xgboost classification recommender recommender-system apriori feature-engineering flink fm flink-ml flink-machine-learning

recommenders - TensorFlow Recommenders is a library for building recommender system models using TensorFlow

•    Python

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TensorFlow Recommenders is a library for building recommender system models using TensorFlow. It helps with the full workflow of building a recommender system: data preparation, model formulation, training, evaluation, and deployment.

tensorflow recommender recommender-system tensorflow-recommenders

moviebox - Machine learning movie recommending system

•    Python

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Moviebox is a content based machine learning recommending system build with the powers of tf-idf and cosine similarities. Initially, a natural number, that corresponds to the ID of a unique movie title, is accepted as input from the user. Through tf-idf the plot summaries of 5000 different movies that reside in the dataset, are analyzed and vectorized. Next, a number of movies is chosen as recommendations based on their cosine similarity with the vectorized input movie. Specifically, the cosine value of the angle between any two non-zero vectors, resulting from their inner product, is used as the primary measure of similarity. Thus, only movies whose story and meaning are as close as possible to the initial one, are displayed to the user as recommendations.

movie recommender machine unsupervised learning tf-idf

ger - Good Enough Recommendation (GER) Engine

•    CoffeeScript

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Providing good recommendations can get greater user engagement and provide an opportunity to add value that would otherwise not exist. The main reason why many applications don't provide recommendations is the difficulty in either implementing a custom engine or using an existing engine. Good Enough Recommendations (GER) is a recommendation engine that is scalable, easily usable and easy to integrate. GER's goal is to generate good enough recommendations for your application or product, so that you can provide value quickly and painlessly.

recommend recommended recommendation engine collaborative filtering recommender

gorse - A High Performance Recommender System Package based on Collaborative Filtering for Go

•    Go

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More examples could be found in the example folder. All models are tested by 5-fold cross validation on a PC with Intel(R) Core(TM) i5-4590 CPU (3.30GHz) and 16.0GB RAM. All scores are the best scores achieved by gorse yet.

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moviebox - 🎥 Machine learning movie recommender

•    Python

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Moviebox is a content based machine learning recommending system build with the powers of tf-idf and cosine similarities.Initially, a natural number, that corresponds to the ID of a unique movie title, is accepted as input from the user. Through tf-idf the plot summaries of 5000 different movies that reside in the dataset, are analyzed and vectorized. Next, a number of movies is chosen as recommendations based on their cosine similarity with the vectorized input movie. Specifically, the cosine value of the angle between any two non-zero vectors, resulting from their inner product, is used as the primary measure of similarity. Thus, only movies whose story and meaning are as close as possible to the initial one, are displayed to the user as recommendations.

movie box recommender machine unsupervised learning content based tf-idf moviebox recommendation-system

recommenderlab - recommenderlab - Lab for Developing and Testing Recommender Algorithms - R package

•    R

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Current development version: Download package from AppVeyor or install from GitHub (needs devtools). Load the package and prepare a dataset (included in the package).

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telecheck - Simple CLI Tool For Generating Available Telegram Usernames

•    Python

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Just fill an issue and describe it. I'll check it ASAP! or send an email to sepand@qpage.ir. Remember to write a few tests for your code before sending pull requests.

telegram usernames generator python3 cli recommendations recommender ids availability available

binge - Recommendation models that use binary rather than floating point operations at prediction time

•    TeX

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This repository contains an implementation of recommendation models that use binary rather than floating point operations at prediction time. This makes them much faster (and less memory intensive), but also less accurate. The details are in the paper and in the slides.

recommender machine-learning matrix-factorization learning-to-rank deep-learning

recommender - NReco Recommender is a

•    CSharp

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NReco Recommender is a .NET port of Apache Mahout Collaborative Filtering engine "Taste" (standalone, non-Hadoop version). Names of namespaces, class names and public methods are preserved but aligned with C# naming conventions. Copyright 2013-2017 Vitalii Fedorchenko (nrecosite.com) Parts of this code are based on Apache Mahout ("Taste") that was licensed under the Apache 2.0 License (see http://www.apache.org/licenses/LICENSE-2.0).

dot-net dotnetcore recommendation-engine recommendation-system collaborative-filtering mahout-cf-engine recommender-system recommendation-algorithms recommender