spotlight - Deep recommender models using PyTorch.

•    Python

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Spotlight uses PyTorch to build both deep and shallow recommender models. By providing both a slew of building blocks for loss functions (various pointwise and pairwise ranking losses), representations (shallow factorization representations, deep sequence models), and utilities for fetching (or generating) recommendation datasets, it aims to be a tool for rapid exploration and prototyping of new recommender models. See the full documentation for details.

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

xlearn - High performance, easy-to-use, and scalable ML package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and command line interface

•    C++

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xLearn is a high performance, easy-to-use, and scalable machine learning package, which can be used to solve large-scale machine learning problems. xLearn is especially useful for solving machine learning problems on large-scale sparse data, which is very common in Internet services such as online advertisement and recommender systems in recent years. If you are the user of liblinear, libfm, or libffm, now xLearn is your another better choice. xLearn is developed with high-performance C++ code with careful design and optimizations. Our system is designed to maximize CPU and memory utilization, provide cache-aware computation, and support lock-free learning. By combining these insights, xLearn is 5x-13x faster compared to similar systems.

machine-learning statistics data-science data-analysis

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

fastFM - fastFM: A Library for Factorization Machines

•    Python

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The library fastFM is an academic project. The time and resources spent developing fastFM are therefore justified by the number of citations of the software. If you publish scientific articles using fastFM, please cite the following article (bibtex entry citation.bib). This repository allows you to use Factorization Machines in Python (2.7 & 3.x) with the well known scikit-learn API. All performance critical code as been written in C and wrapped with Cython. fastFM provides stochastic gradient descent (SGD) and coordinate descent (CD) optimization routines as well as Markov Chain Monte Carlo (MCMC) for Bayesian inference. The solvers can be used for regression, classification and ranking problems. Detailed usage instructions can be found in the online documentation and on arXiv.

machine-learning recommender-system factorization-machines matrix-factorization

implicit - Fast Python Collaborative Filtering for Implicit Feedback Datasets

•    Python

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Fast Python Collaborative Filtering for Implicit Datasets. Alternating Least Squares as described in the papers Collaborative Filtering for Implicit Feedback Datasets and Applications of the Conjugate Gradient Method for Implicit Feedback Collaborative Filtering.

collaborative-filtering machine-learning matrix-factorization recommender-system

universal-recommender - Highly configurable recommender based on PredictionIO and Mahout's Correlated Cross-Occurrence algorithm

•    Scala

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The Universal Recommender (UR) is a new type of collaborative filtering recommender based on an algorithm that can use data from a wide variety of user preference indicators—it is called the Correlated Cross-Occurrence algorithm. Unlike matrix factorization embodied in things like MLlib's ALS, CCO is able to ingest any number of user actions, events, profile data, and contextual information. It then serves results in a fast and scalable way. It also supports item properties for building flexible business rules for filtering and boosting recommendations and can therefor be considered a hybrid collaborative filtering and content-based recommender. Most recommenders can only use conversion events, like buy or rate. Using all we know about a user and their context allows us to much better predict their preferences.

awesome-project-ideas - Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas

•    

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Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas

deep-learning forecasting machine-learning classification series-forecasting image-classification awesome-list awesome dataset multi-label-classification

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

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

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.

recommender-system svd svdplusplus knn slope-one co-clustering nmf machine-learning recommender bpr collaborative-filtering data-mining machinelearning avx2

rumale - Rumale is a machine learning library in Ruby

•    Ruby

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Rumale (Ruby machine learning) is a machine learning library in Ruby. Rumale provides machine learning algorithms with interfaces similar to Scikit-Learn in Python. Rumale supports Linear / Kernel Support Vector Machine, Logistic Regression, Linear Regression, Ridge, Lasso, Kernel Ridge, Factorization Machine, Naive Bayes, Decision Tree, AdaBoost, Gradient Tree Boosting, Random Forest, Extra-Trees, K-nearest neighbor classifier, K-Means, K-Medoids, Gaussian Mixture Model, DBSCAN, SNN, Power Iteration Clustering, Mutidimensional Scaling, t-SNE, Principal Component Analysis, Kernel PCA and Non-negative Matrix Factorization. This project was formerly known as "SVMKit". If you are using SVMKit, please install Rumale and replace SVMKit constants with Rumale.

machine-learning data-science data-analysis artificial-intelligence

Recommenders - Recommender Systems

•    Jupyter

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Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting train/test data. Implementations of several state-of-the-art algorithms are provided for self-study and customization in your own applications. Please see the setup guide for more details on setting up your machine locally, on Spark, or on Azure Databricks.

recommender-system machine-learning-algorithms ranking recommendation-algorithms

seldon-server - Machine Learning Platform and Recommendation Engine built on Kubernetes

•    Java

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This project is not actively maintained anymore please see Seldon Core. Seldon Server is a machine learning platform that helps your data science team deploy models into production.

machine-learning deep-learning deployment kubernetes docker microservices spark kafka kafka-streams tensorflow cloud aws gcp azure seldon recommender-system recommendation-engine prediction

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

•    Python

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This repository contains the code for a wine reviews and recommendations web application, in different stages as git tags. The idea is that you can follow the tutorials through the tags listed below, and learn the different concepts explained in them. The tutorials include instructions on how to deploy the web using a Koding account. However, Koding recently moved from solo to team accounts and the link provided to my Koding account deployment of the tutorial result is not working anymore. The tutorial can still be followed with no problem at all. The following tutorials will guide you through each of the previous Git tags while learning different concepts of data product development with Python.

tutorial django scikit-learn pandas recommender-system wine

lrslibrary - Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos

•    Matlab

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Low-Rank and Sparse tools for Background Modeling and Subtraction in Videos. The LRSLibrary provides a collection of low-rank and sparse decomposition algorithms in MATLAB. The library was designed for motion segmentation in videos, but it can be also used (or adapted) for other computer vision problems (for more information, please see this page). Currently the LRSLibrary offers more than 100 algorithms based on matrix and tensor methods. The LRSLibrary was tested successfully in several MATLAB versions (e.g. R2014, R2015, R2016, R2017, on both x86 and x64 versions). It requires minimum R2014b.

rpca matrix-factorization matrix-completion tensor-decomposition tensor matlab matrix subspace-tracking subspace-learning

Conjecture - Scalable Machine Learning in Scalding

•    Java

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Conjecture is a framework for building machine learning models in Hadoop using the Scalding DSL. The goal of this project is to enable the development of statistical models as viable components in a wide range of product settings. Applications include classification and categorization, recommender systems, ranking, filtering, and regression (predicting real-valued numbers). Conjecture has been designed with a primary emphasis on flexibility and can handle a wide variety of inputs. Integration with Hadoop and scalding enable seamless handling of extremely large data volumes, and integration with established ETL processes. Predicted labels can either be consumed directly by the web stack using the dataset loader, or models can be deployed and consumed by live web code. Currently, binary classification (assigning one of two possible labels to input data points) is the most mature component of the Conjecture package.There are a few stages involved in training a machine learning model using Conjecture.

ytk-learn - Ytk-learn is a distributed machine learning library which implements most of popular machine learning algorithms(GBDT, GBRT, Mixture Logistic Regression, Gradient Boosting Soft Tree, Factorization Machines, Field-aware Factorization Machines, Logistic Regression, Softmax)

•    Java

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Ytk-learn is a distributed machine learning library which implements most of popular machine learning algorithms

machine-learning distributed gbm gbdt logistic-regression factorization-machines spark hadoop

tensorrec - A TensorFlow recommendation algorithm and framework in Python.

•    Python

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A TensorFlow recommendation algorithm and framework in Python. TensorRec is a Python recommendation system that allows you to quickly develop recommendation algorithms and customize them using TensorFlow.

machine-learning tensorflow recommendation-system recommender-system recommendation-algorithm framework

elasticsearch-learning-to-rank - Plugin to integrate Learning to Rank (aka machine learning for better relevance) with Elasticsearch

•    Java

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Rank Elasticsearch results using tree based (LambdaMART, Random Forest, MART) and linear models. Models are trained using the scores of Elasicsearch queries as features. You train offline using tooling such as with xgboost or ranklib. You then POST your model to a to Elasticsearch in a specific text format (the custom "ranklib" language, documented here). You apply a model using this plugin's ltr query. See blog post and the full demo (training and searching).Models are stored using an Elasticsearch script plugin. Tree-based models can be large. So we recommend increasing the script.max_size_in_bytes setting. Don't worry, just because tree-based models are verbose, doesn't nescesarilly imply they'll be slow.

elasticsearch relevant-search machine-learning search-relevance

spark-movie-lens - An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset

•    Jupyter

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This Apache Spark tutorial will guide you step-by-step into how to use the MovieLens dataset to build a movie recommender using collaborative filtering with Spark's Alternating Least Saqures implementation. It is organised in two parts. The first one is about getting and parsing movies and ratings data into Spark RDDs. The second is about building and using the recommender and persisting it for later use in our on-line recommender system. This tutorial can be used independently to build a movie recommender model based on the MovieLens dataset. Most of the code in the first part, about how to use ALS with the public MovieLens dataset, comes from my solution to one of the exercises proposed in the CS100.1x Introduction to Big Data with Apache Spark by Anthony D. Joseph on edX, that is also publicly available since 2014 at Spark Summit. Starting from there, I've added with minor modifications to use a larger dataset, then code about how to store and reload the model for later use, and finally a web service using Flask.

movie-recommendation movielens-dataset spark flask big-data bigdata