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This repository contains Deep Learning based articles , paper and repositories for Recommender Systems

Neural Graph Collaborative Filtering, SIGIR2019

Basic Movie Recommendation Web Application using user-item collaborative filtering.

Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

阅读过的推荐系统论文的归类总结，持续更新中…

Papers about recommendation systems that I am interested in

This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.

Fast and accurate machine learning on sparse matrices - matrix factorizations, regression, classification, top-N recommendations.

A Curated List of Must-read Papers on Recommender System.

📖Notes and remarks on Machine Learning related papers

A Clojure library for querying large data-sets on similarity

An open source recommender system service written in Go

Building recommender systems in Julia

rectorch is a pytorch-based framework for state-of-the-art top-N recommendation

Neural Collaborative Filtering

Accompanying code for reproducing experiments from the HybridSVD paper. Preprint is available at https://arxiv.org/abs/1802.06398.

Recommender system and evaluation framework for top-n recommendations tasks that respects polarity of feedbacks. Fast, flexible and easy to use. Written in python, boosted by scientific python stack.

Fast Python Collaborative Filtering for Implicit Feedback Datasets

Towards open benchmarking for recommender systems https://openbenchmark.github.io/BARS

recommender system tutorial with Python

Collaborative Memory Network for Recommendation Systems, SIGIR 2018

A Comparative Framework for Multimodal Recommender Systems

PyTorch implementations of Top-N recommendation, collaborative filtering recommenders.

Factorization Machines for Recommendation and Ranking Problems with Implicit Feedback Data

Spark SQL 实现 ItemCF，UserCF，Swing，推荐系统，推荐算法，协同过滤

The hybrid model combining stacked denoising autoencoder with matrix factorization is applied, to predict the customer purchase behavior in the future month according to the purchase history and user information in the Santander dataset.

UC Berkeley team's submission for RecSys Challenge 2018

A pure Python implement of Collaborative Filtering based on MovieLens' dataset.

WWW'2019: Modeling Item-Specific Temporal Dynamics of Repeat Consumption for Recommender Systems

BPR, Bayesian Personalized Ranking (BPR), extremely convenient BPR & Multiple Pairwise Ranking

Large scale training of factorization models for Collaborative Filtering with PyTorch

Summary of social recommendation papers and codes

kNN-based next-basket recommendation

个性化新闻推荐系统，A news recommendation system involving collaborative filtering,content-based recommendation and hot news recommendation, can be adapted easily to be put into use in other circumstances.

Implemented User Based and Item based Recommendation System along with state of the art Deep Learning Techniques

A developing recommender system in pytorch. Algorithm: KNN, LFM, SLIM, NeuMF, FM, DeepFM, VAE and so on, which aims to fair comparison for recommender system benchmarks

This is our implementation of ENMF: Efficient Neural Matrix Factorization (TOIS. 38, 2020). This also provides a fair evaluation of existing state-of-the-art recommendation models.

tf-recsys contains collaborative filtering (CF) model based on famous SVD and SVD++ algorithm. Both of them are implemented by tensorflow in order to utilize GPU acceleration.

Disentagnled Graph Collaborative Filtering, SIGIR2020

[ WSDM '19 ] Sequential Variational Autoencoders for Collaborative Filtering

In this code we implement and compared Collaborative Filtering algorithm, prediction algorithms such as neighborhood methods, matrix factorization-based ( SVD, PMF, SVD++, NMF), and many others.

NReco Recommender is a .NET port of Apache Mahout CF java engine (standalone, non-Hadoop version)

A developing recommender system in tensorflow2. Algorithm: UserCF, ItemCF, LFM, SLIM, GMF, MLP, NeuMF, FM, DeepFM, MKR, RippleNet, KGCN and so on.

AI-related tutorials. Access any of them for free → https://towardsai.net/editorial

Deep learning for recommender systems

A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors which can be used for ANN search.

Ranking made easy

毕业设计：基于图神经网络的异构图表示学习和推荐算法研究

Personalized real-time movie recommendation system

PyTorch Implementations For A Series Of Deep Learning-Based Recommendation Models (IN PROGRESS)

An Open-source Toolkit for Deep Learning based Recommendation with Tensorflow.

Using Deep Autoencoders for predictions of movie ratings.

MRSR - Matlab Recommender Systems Research is a software framework for evaluating collaborative filtering recommender systems in Matlab.

Scraping publicly-accessible Letterboxd data and creating a movie recommendation model with it that can generate recommendations when provided with a Letterboxd username

Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach, AAAI2020

Book recommender system using collaborative filtering based on Spark

A unified, comprehensive and efficient recommendation library

TensorFlow Implementation of Deep Item-based Collaborative Filtering Model for Top-N Recommendation

Next RecSys Library

Factorization Machine models in PyTorch