Displaying 1 to 20 from 25 results

implicit - Fast Python Collaborative Filtering for Implicit Feedback Datasets

  •    Python

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.

spotlight - Deep recommender models using PyTorch.

  •    Python

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.

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.

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).




neanderthal - Fast Clojure Matrix Library

  •    Clojure

Neanderthal is a Clojure library for fast matrix and linear algebra computations based on the highly optimized native libraries of BLAS and LAPACK computation routines for both CPU and GPU.. Read the documentation at Neanderthal Web Site.

fastFM - fastFM: A Library for Factorization Machines

  •    Python

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.

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

  •    Matlab

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.


nimfa - Nimfa: Nonnegative matrix factorization in Python

  •    Python

Nimfa is a Python module that implements many algorithms for nonnegative matrix factorization. Nimfa is distributed under the BSD license. The project was started in 2011 by Marinka Zitnik as a Google Summer of Code project, and since then many volunteers have contributed. See AUTHORS file for a complete list of contributors.

scikit-fusion - scikit-fusion: Data fusion in Python

  •    Python

scikit-fusion is a Python module for data fusion based on recent collective latent factor models. scikit-fusion is tested to work under Python 3.

recosystem - Recommender System Using Parallel Matrix Factorization

  •    C++

The API of this package has changed since version 0.4, due to the API change of LIBMF 2.01 and some other design improvement. recosystem is an R wrapper of the LIBMF library developed by Yu-Chin Juan, Wei-Sheng Chin, Yong Zhuang, Bo-Wen Yuan, Meng-Yuan Yang, and Chih-Jen Lin (http://www.csie.ntu.edu.tw/~cjlin/libmf/), an open source library for recommender system using parallel matrix factorization.

m4ri - MIRROR: M4RI is a library for fast arithmetic with dense matrices over GF(2)

  •    C

M4RI is a library for fast arithmetic with dense matrices over F2. The name M4RI comes from the first implemented algorithm: The “Method of the Four Russians” inversion algorithm published by Gregory Bard. This algorithm in turn is named after the “Method of the Four Russians” multiplication algorithm which is probably better referred to as Kronrod's method. M4RI is available under the General Public License Version 2 or later (GPLv2+). and support for Linux, Solaris, and OS X (GCC).

recommend - recommendation system with python

  •    Python

A jupyter notbook that compares PMF and BPMF model can be found here. Old version code can be found in v0.0.1. It contains a Probabilistic Matrix Factorization model with theano implementation.

cofactor - CoFactor: Regularizing Matrix Factorization with Item Co-occurrence

  •    Jupyter

This repository contains the source code to reproduce the experimental results as described in the paper "Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence" (RecSys'16). Note: The code is mostly written for Python 2.7. For Python 3.x, it is still usable with minor modification. If you run into any problem with Python 3.x, feel free to contact me and I will try to get back to you with a helpful solution.

expo-mf - Exposure Matrix Factorization: modeling user exposure in recommendation

  •    Jupyter

This repository contains the source code to reproduce all the experimental results as described in the paper "Modeling User Exposure in Recommendation" (WWW'16). Note: The code is mostly written for Python 2.7. For Python 3.x, it is still usable with minor modification. If you run into any problem with Python 3.x, feel free to contact me and I will try to get back to you with a helpful solution.

rsparse - Fast and accurate machine learning on sparse matrices - Factorization Machines, FTRL, Matrix factorizations

  •    R

rsparse is an R package for statistical learning on sparse data. Notably it implements many algorithms sparse matrix factorizations with a focus on applications for recommender systems. All of the algorithms benefit from OpenMP and most of them use BLAS. Package scales nicely to datasets with millions of rows and millions of columns.

proNet-core - A general-purpose network embedding framework: pair-wise representations optimization Network

  •    C++

In the near future, we will redesign the framework making some solid APIs for fast development on different network embedding techniques. This shell script will help obtain the representations of the Youtube links in Youtube-links dataset.

flurs - :ocean: FluRS: A Python library for streaming recommendation algorithms

  •    Python

FluRS is a Python library for online item recommendation. The name indicates Flu-* (Flux, Fluid, Fluent) Recommender Systems which incrementally adapt to dynamic user-item interactions in a streaming environment. Note that repository takuti/stream-recommender uses FluRS v0.0.1 to write research papers.