Displaying 1 to 13 from 13 results

DeepRecommender - Deep learning for recommender systems

  •    Python

The model is based on deep AutoEncoders. The code is intended to run on GPU. Last test can take a minute or two.

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.

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.

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.

RecommendationEngine - A creative recommendation engine based on Hadoop, powered by an efficient and high scalable implementation of item-based collaborative filtering recommendation algorithm

  •    Java

The key of Recommendation Engine is an efficient and scalable implementation of item-based collaborative filtering (CF) recommendation algorithm based on Hadoop.Item-based CF algorithm has become one of the most popular algorithms in recommendation systems. However, the item-based CF algorithm has been traditionally run in stand-alone mode and can be hindered by some hardware constraints, such as memory and computational limitations. Besides, in recent years recommendation systems are usually required to process large volumes of information with high dimensions, which poses some key challenges to provide recommendations quickly. So despite some excellent algorithms like item based CF running well in stand-alone mode, there is an impracticality in the condition of huge amount of users and items. This is the scalability problem and whether it can be solved properly determines the further development of recommendation systems.

Recommender - A C library for product recommendations/suggestions using collaborative filtering (CF)

  •    C

A C library for product recommendations/suggestions using collaborative filtering (CF). Recommender analyzes the feedback of some users (implicit and explicit) and their preferences for some items. It learns patterns and predicts the most suitable products for a particular user.

vae_cf - Variational autoencoders for collaborative filtering

  •    Jupyter

This notebook accompanies the paper "Variational autoencoders for collaborative filtering" by Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara, in The Web Conference (aka WWW) 2018. In this notebook, we show a complete self-contained example of training a variational autoencoder (as well as a denoising autoencoder) with multinomial likelihood (described in the paper) on the public Movielens-20M dataset, including both data preprocessing and model training.

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.

Neural-Networks-for-Collaborative-Filtering - Deep Learning for Recommendation

  •    Python

Required packages : Tensorflow 0.10 Python 3.5 Scikit-learn 0.17 Numpy Scipy The code was developped under this versions of the packages, it might be incompatible with some newer versions due to the depreciation of some functions. You can change the experiment parameters under the config/ subfolder.

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).