Displaying 1 to 13 from 13 results

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

  •    Java

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.

Surprise - A Python scikit for building and analyzing recommender systems

  •    Python

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.

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

  •    Python

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).




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

  •    Python

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.

moviebox - Machine learning movie recommending system

  •    Python

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.

ger - Good Enough Recommendation (GER) Engine

  •    CoffeeScript

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.

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

  •    Go

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.


moviebox - 🎥 Machine learning movie recommender

  •    Python

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.

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

  •    R

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

telecheck - Simple CLI Tool For Generating Available Telegram Usernames

  •    Python

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.

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

  •    TeX

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 - NReco Recommender is a

  •    CSharp

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).






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