Crab - A Python Library for Recommendation Engines This library intends to be a reference for recommendation engines in Python Programming language. It is written in pure python to maximize the cross-platform issue and exposes the recommendation logic to your application by easy to use API REST via web services. The library is extensible, so the user can create new representations, algorithms and the design is optimized for performance. It is also open-source so everyone can use it. If you want to see our plan release/roadmap, please take a look at our Issues Tracker: http://github.com/marcelcaraciolo/crab/issues
This library intends to be a reference for recommendation engines in Python
Overview
A Library for Field-aware Factorization Machines
Table of Contents ================= - What is LIBFFM - Overfitting and Early Stopping - Installation - Data Format - Command Line Usage - Examples -
[ICDMW 2020] Code and dataset for "DGTN: Dual-channel Graph Transition Network for Session-based Recommendation"
DGTN: Dual-channel Graph Transition Network for Session-based Recommendation This repository contains PyTorch Implementation of ICDMW 2020 (NeuRec @ I
Plex-recommender - Get movie recommendations based on your current PleX library
plex-recommender Description: Get movie/tv recommendations based on your current
The source code for "Global Context Enhanced Graph Neural Network for Session-based Recommendation".
GCE-GNN Code This is the source code for SIGIR 2020 Paper: Global Context Enhanced Graph Neural Networks for Session-based Recommendation. Requirement
The implementation of the submitted paper "Deep Multi-Behaviors Graph Network for Voucher Redemption Rate Prediction" in SIGKDD 2021 Applied Data Science Track.
DMBGN: Deep Multi-Behaviors Graph Networks for Voucher Redemption Rate Prediction The implementation of the accepted paper "Deep Multi-Behaviors Graph
Collaborative variational bandwidth auto-encoder (VBAE) for recommender systems.
Collaborative Variational Bandwidth Auto-encoder The codes are associated with the following paper: Collaborative Variational Bandwidth Auto-encoder f
QRec: A Python Framework for quick implementation of recommender systems (TensorFlow Based)
QRec is a Python framework for recommender systems (Supported by Python 3.7.4 and Tensorflow 1.14+) in which a number of influential and newly state-of-the-art recommendation models are implemented.
Respiratory Health Recommendation System
Respiratory-Health-Recommendation-System Respiratory Health Recommendation System based on Air Quality Index Forecasts This project aims to provide pr
Code for my ORSUM, ACM RecSys 2020, HeroGRAPH: A Heterogeneous Graph Framework for Multi-Target Cross-Domain Recommendation
HeroGRAPH Code for my ORSUM @ RecSys 2020, HeroGRAPH: A Heterogeneous Graph Framework for Multi-Target Cross-Domain Recommendation Paper, workshop pro
Cloud-based recommendation system
This project is based on cloud services to create data lake, ETL process, train and deploy learning model to implement a recommendation system.
Code for KHGT model, AAAI2021
KHGT Code for KHGT accepted by AAAI2021 Please unzip the data files in Datasets/ first. To run KHGT on Yelp data, use python labcode_yelp.py For Movi
RecList is an open source library providing behavioral, "black-box" testing for recommender systems.
RecList is an open source library providing behavioral, "black-box" testing for recommender systems.
Pytorch domain library for recommendation systems
TorchRec (Experimental Release) TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale
A library of Recommender Systems
A library of Recommender Systems This repository provides a summary of our research on Recommender Systems. It includes our code base on different rec
Deep recommender models using PyTorch.
Spotlight uses PyTorch to build both deep and shallow recommender models. By providing both a slew of building blocks for loss functions (various poin
A framework for large scale recommendation algorithms.
A framework for large scale recommendation algorithms.
Incorporating User Micro-behaviors and Item Knowledge 59 60 3 into Multi-task Learning for Session-based Recommendation
MKM-SR Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation Paper data and code This is the
Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction
MGNN-SPred This is our Tensorflow implementation for the paper: WenWang,Wei Zhang, Shukai Liu, Qi Liu, Bo Zhang, Leyu Lin, and Hongyuan Zha. 2020. Bey
Hierarchical Fashion Graph Network for Personalized Outfit Recommendation, SIGIR 2020
hierarchical_fashion_graph_network This is our Tensorflow implementation for the paper: Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, and
Recommender System Papers
Included Conferences: SIGIR 2020, SIGKDD 2020, RecSys 2020, CIKM 2020, AAAI 2021, WSDM 2021, WWW 2021