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

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

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

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

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

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)

•    Python

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Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recom- mendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib). The engine aims to provide a rich set of components from which you can construct a customized recommender system from a set of algorithms. The project was started in 2010 by Marcel Caraciolo as a M.S.C related project, and since then many people interested joined to help in the project. It is currently maintained by a team of volunteers, members of the Muriçoca Labs.

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

PaddleFL - Federated Deep Learning in PaddlePaddle

•    C++

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PaddleFL is an open source federated learning framework based on PaddlePaddle. Researchers can easily replicate and compare different federated learning algorithms with PaddleFL. Developers can also benefit from PaddleFL in that it is easy to deploy a federated learning system in large scale distributed clusters. In PaddleFL, several federated learning strategies will be provided with application in computer vision, natural language processing, recommendation and so on. Application of traditional machine learning training strategies such as Multi-task learning, Transfer Learning in Federated Learning settings will be provided. Based on PaddlePaddle's large scale distributed training and elastic scheduling of training job on Kubernetes, PaddleFL can be easily deployed based on full-stack open sourced software. Data is becoming more and more expensive nowadays, and sharing of raw data is very hard across organizations. Federated Learning aims to solve the problem of data isolation and secure sharing of data knowledge among organizations. The concept of federated learning is proposed by researchers in Google [1, 2, 3]. PaddleFL implements federated learning based on the PaddlePaddle framework. Application demonstrations in natural language processing, computer vision and recommendation will be provided in PaddleFL. PaddleFL supports the current two main federated learning strategies[4]: vertical federated learning and horizontal federated learning. Multi-tasking learning [7] and transfer learning [8] in federated learning will be developed and supported in PaddleFL in the future.

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

Hyperparameter-Optimization-of-Machine-Learning-Algorithms - Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

•    Jupyter

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This code provides a hyper-parameter optimization implementation for machine learning algorithms, as described in the paper: L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295–316, 2020, doi: https://doi.org/10.1016/j.neucom.2020.07.061. To fit a machine learning model into different problems, its hyper-parameters must be tuned. Selecting the best hyper-parameter configuration for machine learning models has a direct impact on the model's performance. In this paper, optimizing the hyper-parameters of common machine learning models is studied. We introduce several state-of-the-art optimization techniques and discuss how to apply them to machine learning algorithms. Many available libraries and frameworks developed for hyper-parameter optimization problems are provided, and some open challenges of hyper-parameter optimization research are also discussed in this paper. Moreover, experiments are conducted on benchmark datasets to compare the performance of different optimization methods and provide practical examples of hyper-parameter optimization.

machine-learning deep-learning random-forest optimization svm genetic-algorithm machine-learning-algorithms hyperparameter-optimization artificial-neural-networks grid-search tuning-parameters knn bayesian-optimization hyperparameter-tuning random-search particle-swarm-optimization hpo python-examples python-samples hyperband

Vespa - Yahoo's big data serving engine

•    Java

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Vespa is an engine for low-latency computation over large data sets. It stores and indexes your data such that queries, selection and processing over the data can be performed at serving time. Vespa is serving platform for Yahoo.com, Yahoo News, Yahoo Sports, Yahoo Finance, Yahoo Gemini, Flickr.

searchengine search-engine big-data data-processing machine-learning real-time

recsim - A Configurable Recommender Systems Simulation Platform

•    Python

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RecSim is a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users. RecSim allows the creation of new environments that reflect particular aspects of user behavior and item structure at a level of abstraction well-suited to pushing the limits of current reinforcement learning (RL) and RS techniques in sequential interactive recommendation problems. Environments can be easily configured that vary assumptions about: user preferences and item familiarity; user latent state and its dynamics; and choice models and other user response behavior. We outline how RecSim offers value to RL and RS researchers and practitioners, and how it can serve as a vehicle for academic-industrial collaboration. For a detailed description of the RecSim architecture please read Ie et al. Please cite the paper if you use the code from this repository in your work. This is not an officially supported Google product.

machine-learning google reinforcement-learning simulation tensorflow artificial-intelligence recommender-system

Reco-papers - Classic papers and resources on recommendation

•    Python

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Classic papers and resources on recommendation

machine-learning reinforcement-learning deep-learning recommender-system recommendation exploration-exploitation

machine_learning_basics - Plain python implementations of basic machine learning algorithms

•    Jupyter

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This repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, not to provide the most efficient implementations. After several requests I started preparing notebooks on how to preprocess datasets for machine learning. Within the next months I will add one notebook for each kind of dataset (text, images, ...). As before, the intention of these notebooks is to provide a basic understanding of the preprocessing steps, not to provide the most efficient implementations.

machine-learning logistic-regression ipynb machine-learning-algorithms linear-regression perceptron python-implementations kmeans algorithm python3 neural-network k-nearest-neighbours k-nearest-neighbor k-nn neural-networks

Math-of-Machine-Learning-Course-by-Siraj - Implements common data science methods and machine learning algorithms from scratch in python

•    Jupyter

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This repository was initially created to submit machine learning assignments for Siraj Raval's online machine learning course. The purpose of the course was to learn how to implement the most common machine learning algorithms from scratch (without using machine learning libraries such as tensorflow, PyTorch, scikit-learn, etc). Although that course has ended now, I am continuing to learn data science and machine learning from other sources such as Coursera, online blogs, and attending machine learning lectures at University of Toronto. Sticking to the theme of implementing machine learning algortihms from scratch, I will continue to post detailed notebooks in python here as I learn more.

machine-learning statistical-concepts siraj-raval machine-learning-algorithms machine-learning-from-scratch

MLAlgorithms - Minimal and clean examples of machine learning algorithms implementations

•    Python

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A collection of minimal and clean implementations of machine learning algorithms. This project is targeting people who want to learn internals of ml algorithms or implement them from scratch. The code is much easier to follow than the optimized libraries and easier to play with. All algorithms are implemented in Python, using numpy, scipy and autograd.

machine-learning deep-learning neural-networks machine-learning-algorithms

Smile - Statistical Machine Intelligence & Learning Engine

•    Java

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Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. With advanced data structures and algorithms, Smile delivers state-of-art performance.Smile covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc.

machine-learning nlp linear-algebra natural-language-processing

ranking - Learning to Rank in TensorFlow

•    Python

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We envision that this library will provide a convenient open platform for hosting and advancing state-of-the-art ranking models based on deep learning techniques, and thus facilitate both academic research and industrial applications. TF-Ranking was presented at premier conferences in Information Retrieval, SIGIR 2019 and ICTIR 2019! The slides are available here.

machine-learning information-retrieval deep-learning ranking learning-to-rank recommender-systems

Play-with-Machine-Learning-Algorithms - Code of my MOOC Course <Play with Machine Learning Algorithms>

•    Jupyter

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Code of my MOOC Course <Play with Machine Learning Algorithms>. Updated contents and practices are also included. 我在慕课网上的课程《Python3 入门机器学习》示例代码。课程的更多更新内容及辅助练习也将逐步添加进这个代码仓。

machine-learning-algorithms machine-learning mooc imooc jupyter-notebooks

ABAGAIL - The library contains a number of interconnected Java packages that implement machine learning and artificial intelligence algorithms

•    Java

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The library contains a number of interconnected Java packages that implement machine learning and artificial intelligence algorithms. These are artificial intelligence algorithms implemented for the kind of people that like to implement algorithms themselves. See Issues page.

artificial-intelligence-algorithms neural-network machine-learning optimization-algorithms