Displaying 1 to 20 from 33 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.

Recommenders - Recommender Systems

  •    Jupyter

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.

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.




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).

tensorrec - A TensorFlow recommendation algorithm and framework in Python.

  •    Python

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.

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.

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

  •    Python

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.


Recommendation Engine Demo

  •    

How does the Amazon recommendation works? This is about visualizing the item to item collaborations filtering mechanism using a item-to-item matrix table. The item-to-item matrix, the vectors and the calculated data values are displayed. There are n different items and...

awesome-RecSys-papers - The awesome and classic papers in recommendation system!!! Good luck to every RecSys-learner!

  •    

The topic of my dissertation is recommendation system. I collected some classic and awesome papers here. Good luck to every RecSys-learner. My email is ZhangYuyang4d@163.com. If you find any mistakes, or you have some suggestions, just send a email to me.

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.

elastic-graph-recommender - Building recommenders with Elastic Graph!

  •    Javascript

Building recommenders with Elastic Graph! This app makes movie recommendations using Elastic graph based on the Movielens data set. Movielens is a well known open data set with user movie ratings.We use this data alongside The Movie Database(TMDB). TMDB has all the movie details such as title, image URL, etc.

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.

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.

szuthesis - :pencil: SZU Undergraduate Thesis -- Recommender System

  •    PostScript

论文主要关于推荐系统,重点研究在利用隐式反馈的推荐算法上如何融合内容信息, 算法模型为 Bayesian Personalized Ranking + Content,可以 点击这里 查看论文. 论文使用 LaTeX 撰写,对于 LaTeX 初学者撰写论文应当有一定借鉴意义.

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.

consimilo - A Clojure library for querying large data-sets on similarity

  •    Clojure

consimilo is a library that utilizes locality sensitive hashing (implemented as lsh-forest) and minhashing, to support top-k similar item queries. Finding similar items across expansive data-sets is a common problem that presents itself in many real world applications (e.g. finding articles from the same source, plagiarism detection, collaborative filtering, context filtering, document similarity, etc...). Searching a corpus for top-k similar items quickly grows to an unwieldy complexity at relatively small corpus sizes (n choose 2). LSH reduces the search space by "hashing" items in such a way that collisions occur as a result of similarity. Once the items are hashed and indexed the lsh-forest supports a top-k most similar items query of ~O(log n). There is an accuracy trade-off that comes with the enormous increase in query speed. More information can be found in chapter 3 of Mining Massive Datasets. You can continue to add to this forest by passing it as the first argument to add-all-to-forest. The forest data structure is stored in an atom, so the existing forest is modified in place.

FMG - KDD17_FMG

  •    Matlab

For the sake of ease, a quick instruction is given for readers to reproduce the whole process on yelp-50k dataset. Note that the programs are testd on Linux(CentOS release 6.9), Python 2.7 from Anaconda 4.3.6. One dependent lib is bottleneck, you may install it with "pip install bottleneck".